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Introduction: Nowadays, the demand for physical therapy services has grown significantly over the last few decades due to an aging population, a rise in chronic conditions, and increased awareness of the benefits of physical therapy for injury recovery and managing various health issues. Collecting and managing data from physical therapy services is highly significant and beneficial. One of the information management systems that facilitates data collection related to physical therapy services is a physical therapy registry. In this systematic review, we aimed to identify physical therapy registries worldwide and examine the characteristics and data elements of each registry. Methods: PubMed, Scopus, Web of Science, and IEEE databases were searched from inception until March 19, 2023 by using keywords and Medical Subject Headings (MeSH) terms regarding "registries" and "physical therapy." The criteria for inclusion in the study were: (1) studies with the English language; (2) original studies, and online access to the physical therapy registry is available; (3) full-text available; (4) studies related to the aims of the study, and (5) studies that have sufficient available information regarding the minimum datasets and other characteristics physical therapy registry. The methodological quality of the included studies was independently assessed by two reviewers using the Effective Public Health Practice Project's (EPHPP) quality assessment tool. Results: Sixteen studies were eligible to be included. The findings of this review indicated that the oldest physical therapy registry was established in 1992, while the newest one was established in 2017. The USA has the highest number of physical therapy registries (n = 7). Ten registries were funded by the government, and the data source for most registries was collected in clinics (n = 11). The geographical coverage of 10 registries was national. All registries collected administrative data (such as sociodemographic data, healthcare provider's data, and others) and clinical data (such as diagnosis, type of physical therapy intervention, pain location, comorbidities, and others) through web-based systems. The data collection method in half of the registries was retrospective (n = 8 out of 16). According to the EPHPP quality assessment tool, 11 studies (73%) were rated as moderate, 3 (20%) as weak, and 1 (7%) as strong. Conclusion: This systematic review found that most developed countries have implemented web-based physical therapy registries to collect administrative and clinical data at the national level. It is recommended that developing countries design and implement similar registries based on these characteristics and extracted data elements. Additionally, these registries should be designed to enable data sharing and interoperability with other international health information systems.

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BACKGROUND: Currently, there are no agreed quality standards for post-stroke aphasia services. Therefore, it is unknown if care reflects best practices or meets the expectations of people living with aphasia. We aimed to (1) shortlist, (2) operationalise and (3) prioritise best practice recommendations for post-stroke aphasia care. METHODS: Three phases of research were conducted. In Phase 1, recommendations with strong evidence and/or known to be important to people with lived experience of aphasia were identified. People with lived experience and health professionals rated the importance of each recommendation through a two-round e-Delphi exercise. Recommendations were then ranked for importance and feasibility and analysed using a graph theory-based voting system. In Phase 2, shortlisted recommendations from Phase 1 were converted into quality indicators for appraisal and voting in consensus meetings. In Phase 3, priorities for implementation were established by people with lived experience and health professionals following discussion and anonymous voting. FINDINGS: In Phase 1, 23 best practice recommendations were identified and rated by people with lived experience (n = 26) and health professionals (n = 81). Ten recommendations were shortlisted. In Phase 2, people with lived experience (n = 4) and health professionals (n = 17) reached a consensus on 11 quality indicators, relating to assessment (n = 2), information provision (n = 3), communication partner training (n = 3), goal setting (n = 1), person and family-centred care (n = 1) and provision of treatment (n = 1). In Phase 3, people with lived experience (n = 5) and health professionals (n = 7) identified three implementation priorities: assessment of aphasia, provision of aphasia-friendly information and provision of therapy. INTERPRETATION: Our 11 quality indicators and 3 implementation priorities are the first step to enabling systematic, efficient and person-centred measurement and quality improvement in post-stroke aphasia services. Quality indicators will be embedded in routine data collection systems, and strategies will be developed to address implementation priorities. PATIENT AND PUBLIC CONTRIBUTION: Protocol development was informed by our previous research, which explored the perspectives of 23 people living with aphasia about best practice aphasia services. Individuals with lived experience of aphasia participated as expert panel members in our three consensus meetings. We received support from consumer advisory networks associated with the Centre for Research Excellence in Aphasia Rehabilitation and Recovery and the Queensland Aphasia Research Centre.

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INTRODUCTION: To support research projects that require medical data from multiple sites is one of the goals of the German Medical Informatics Initiative (MII). The data integration centers (DIC) at university medical centers in Germany provide patient data via FHIR® in compliance with the MII core data set (CDS). Requirements for data protection and other legal bases for processing prefer decentralized processing of the relevant data in the DICs and the subsequent exchange of aggregated results for cross-site evaluation. METHODS: Requirements from clinical experts were obtained in the context of the MII use case INTERPOLAR. A software architecture was then developed, modeled using 3LGM2, finally implemented and published in a github repository. RESULTS: With the CDS tool chain, we have created software components for decentralized processing on the basis of the MII CDS. The CDS tool chain requires access to a local FHIR endpoint and then transfers the data to an SQL database. This is accessed by the DataProcessor component, which performs calculations with the help of rules (input repo) and writes the results back to the database. The CDS tool chain also has a frontend module (REDCap), which is used to display the output data and calculated results, and allows verification, evaluation, comments and other responses. This feedback is also persisted in the database and is available for further use, analysis or data sharing in the future. DISCUSSION: Other solutions are conceivable. Our solution utilizes the advantages of an SQL database. This enables flexible and direct processing of the stored data using established analysis methods. Due to the modularization, adjustments can be made so that it can be used in other projects. We are planning further developments to support pseudonymization and data sharing. Initial experience is being gathered. An evaluation is pending and planned.

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BACKGROUND: The number of patients with Alzheimer's disease (AD) has increased dramatically in Asia. OBJECTIVE: To update the demographic characteristics of patients with AD and their informants in eight Asian countries and compare them from 12 years prior. METHODS: The A1-A3 components of the Uniform Dataset (UDS), version 3.0, were administered in Taiwan, Beijing, Hong Kong, Korea, Japan, Philippines, Thailand, and Indonesia. Data were compared with patients with AD in the first registration using the UDS version 1.0 from 2010-2014 in the same regions. RESULTS: A total of 1885 patients with AD and their informants were recruited from 2022 to 2024 and were compared with 2042 patients recruited a decade prior. Each country had its own unique characteristics that changed between both eras. The mean age of the patients and informants was 79.8±8.2 years and 56.5±12.1 years, respectively. Compared with the first registration, the patients were older (79.8 vs 79.0, p=0.002) and had worse global function (mean CDR-SB scores 6.1 vs 5.8, p<0.001); more informants were children (56 % vs. 48 %, p<0.001), and their frequency of in-person visits increased significantly if not living together. A total of 11 %, 4.5 %, 11 %, and 0.4 % of the patients had a reported history of cognitive impairment in their mothers, fathers, siblings, and children, respectively; all percentages, except children, increased significantly over the past decade. CONCLUSION: The present study reports the heterogeneous characteristics of patients with AD and their informants in Asian countries, and the distinct changes in the past decade. The differences in dementia evaluation and care between developing and developed countries warrant further investigation.

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BACKGROUND: Geriatric assessment (GA) is a multidimensional process that disrupts the primary health care (PHC) referral system. Accessing consistent data is central to the provision of integrated geriatric care across multiple healthcare settings. However, due to poor-quality data and documentation of GA, developing an agreed minimum data set (MDS) is required. Therefore, this study aimed to develop a GA-MDS in the PHC referral system to improve data quality, data exchange, and continuum of care to address the multifaceted necessities of older people. METHODS: In our study, the items to be included within GA-MDS were determined in a three-stepwise process. First, an exploratory literature search was done to determine the related items. Then, we used a two-round Delphi survey to obtain an agreement view on items to be contained within GA-MDS. Finally, the validity of the GA-MDS content was evaluated. RESULTS: Sixty specialists from different health geriatric care disciplines scored data items. After, the Delphi phase from the 230 selected items, 35 items were removed by calculating the content validity index (CVI), content validity ratio (CVR), and other statistical measures. Finally, GA-MDS was prepared with 195 items and four sections including administrative data, clinical, physiological, and psychological assessments. CONCLUSIONS: The development of GA-MDS can serve as a platform to inform the geriatric referral system, standardize the GA process, and streamline their referral to specialized levels of care. We hope GA-MDS supports clinicians, researchers, and policymakers by providing aggregated data to inform medical practice and enhance patient-centered outcomes.

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BACKGROUND: Vaccine hesitancy is a growing global health threat that is increasingly studied through the monitoring and analysis of social media platforms. One understudied area is the impact of echo chambers and influential users on disseminating vaccine information in social networks. Assessing the temporal development of echo chambers and the influence of key users on their growth provides valuable insights into effective communication strategies to prevent increases in vaccine hesitancy. This also aligns with the World Health Organization's (WHO) infodemiology research agenda, which aims to propose new methods for social listening. OBJECTIVE: Using data from a Taiwanese forum, this study aims to examine how engagement patterns of influential users, both within and across different COVID-19 stances, contribute to the formation of echo chambers over time. METHODS: Data for this study come from a Taiwanese forum called PTT. All vaccine-related posts on the "Gossiping" subforum were scraped from January 2021 to December 2022 using the keyword "vaccine." A multilayer network model was constructed to assess the existence of echo chambers. Each layer represents either provaccination, vaccine hesitant, or antivaccination posts based on specific criteria. Layer-level metrics, such as average diversity and Spearman rank correlations, were used to measure chambering. To understand the behavior of influential users-or key nodes-in the network, the activity of high-diversity and hardliner nodes was analyzed. RESULTS: Overall, the provaccination and antivaccination layers are strongly polarized. This trend is temporal and becomes more apparent after November 2021. Diverse nodes primarily participate in discussions related to provaccination topics, both receiving comments and contributing to them. Interactions with the antivaccination layer are comparatively minimal, likely due to its smaller size, suggesting that the forum is a "healthy community." Overall, diverse nodes exhibit cross-cutting engagement. By contrast, hardliners in the vaccine hesitant and antivaccination layers are more active in commenting within their own communities. This trend is temporal, showing an increase during the Omicron outbreak. Hardliner activity potentially reinforces their stances over time. Thus, there are opposing forces of chambering and cross-cutting. CONCLUSIONS: Efforts should be made to moderate hardliner and influential nodes in the antivaccination layer and to support provaccination users engaged in cross-cutting exchanges. There are several limitations to this study. One is the bias of the platform used, and another is the lack of a comprehensive definition of "influence." To address these issues, comparative studies across different platforms can be conducted, and various metrics of influence should be explored. Additionally, examining the impact of influential users on network structure and chambering through network simulations and regression analysis provides more robust insights. The study also lacks an explanation for the reasons behind chambering trends. Conducting content analysis can help to understand the nature of engagement and inform interventions to address echo chambers. These approaches align with and further the WHO infodemic research agenda.

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Hyperspectral Raman imaging not only offers spectroscopic fingerprints but also reveals morphological information such as spatial distributions in an analytical sample. However, the spectrum-per-pixel nature of hyperspectral imaging (HSI) results in a vast amount of data. Furthermore, HSI often requires pre- and post-processing steps to extract valuable chemical information. To derive pure spectral signatures and concentration abundance maps of the active spectroscopic compounds, both endmember extraction (EX) and Multivariate Curve Resolution (MCR) techniques are widely employed. The objective of this study is to carry out a systematic investigation based on Raman mapping datasets to highlight the similarities and differences between these two approaches in retrieving pure variables, and ultimately provide guidelines for pure variable extraction. Numerical simulations and Raman mapping experiments on a mixture of pharmaceutical powders and on a layered plastic foil sample were conducted to underscore the distinctions between MCR and EX algorithms (in particular Vertex Component Analysis, VCA) and their outputs. Both methods were found to perform well if the dataset contains pure pixels for each of the individual components. However, in cases where such pure pixels do not exist, only MCR was found to be capable of extracting the pure component spectra.

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Background: The application of machine learning in health care often necessitates the use of hierarchical codes such as the International Classification of Diseases (ICD) and Anatomical Therapeutic Chemical (ATC) systems. These codes classify diseases and medications, respectively, thereby forming extensive data dimensions. Unsupervised feature selection tackles the "curse of dimensionality" and helps to improve the accuracy and performance of supervised learning models by reducing the number of irrelevant or redundant features and avoiding overfitting. Techniques for unsupervised feature selection, such as filter, wrapper, and embedded methods, are implemented to select the most important features with the most intrinsic information. However, they face challenges due to the sheer volume of ICD and ATC codes and the hierarchical structures of these systems. Objective: The objective of this study was to compare several unsupervised feature selection methods for ICD and ATC code databases of patients with coronary artery disease in different aspects of performance and complexity and select the best set of features representing these patients. Methods: We compared several unsupervised feature selection methods for 2 ICD and 1 ATC code databases of 51,506 patients with coronary artery disease in Alberta, Canada. Specifically, we used the Laplacian score, unsupervised feature selection for multicluster data, autoencoder-inspired unsupervised feature selection, principal feature analysis, and concrete autoencoders with and without ICD or ATC tree weight adjustment to select the 100 best features from over 9000 ICD and 2000 ATC codes. We assessed the selected features based on their ability to reconstruct the initial feature space and predict 90-day mortality following discharge. We also compared the complexity of the selected features by mean code level in the ICD or ATC tree and the interpretability of the features in the mortality prediction task using Shapley analysis. Results: In feature space reconstruction and mortality prediction, the concrete autoencoder-based methods outperformed other techniques. Particularly, a weight-adjusted concrete autoencoder variant demonstrated improved reconstruction accuracy and significant predictive performance enhancement, confirmed by DeLong and McNemar tests (P<.05). Concrete autoencoders preferred more general codes, and they consistently reconstructed all features accurately. Additionally, features selected by weight-adjusted concrete autoencoders yielded higher Shapley values in mortality prediction than most alternatives. Conclusions: This study scrutinized 5 feature selection methods in ICD and ATC code data sets in an unsupervised context. Our findings underscore the superiority of the concrete autoencoder method in selecting salient features that represent the entire data set, offering a potential asset for subsequent machine learning research. We also present a novel weight adjustment approach for the concrete autoencoders specifically tailored for ICD and ATC code data sets to enhance the generalizability and interpretability of the selected features.

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As an indispensable part of the vehicle environment perception task, road traffic marking detection plays a vital role in correctly understanding the current traffic situation. However, the existing traffic marking detection algorithms still have some limitations. Taking lane detection as an example, the current detection methods mainly focus on the location information detection of lane lines, and they only judge the overall attribute of each detected lane line instance, thus lacking more fine-grained dynamic detection of lane line attributes. In order to meet the needs of intelligent vehicles for the dynamic attribute detection of lane lines and more perfect road environment information in urban road environment, this paper constructs a fine-grained attribute detection method for lane lines, which uses pixel-level attribute sequence points to describe the complete attribute distribution of lane lines and then matches the detection results of the lane lines. Realizing the attribute judgment of different segment positions of lane instances is called the fine-grained attribute detection of lane lines (Lane-FGA). In addition, in view of the lack of annotation information in the current open-source lane data set, this paper constructs a lane data set with both lane instance information and fine-grained attribute information by combining manual annotation and intelligent annotation. At the same time, a cyclic iterative attribute inference algorithm is designed to solve the difficult problem of lane attribute labeling in areas without visual cues such as occlusion and damage. In the end, the average accuracy of the proposed algorithm reaches 97% on various types of lane attribute detection.

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BACKGROUND: Older adults with severe aortic stenosis (AS) may receive care in a nursing home (NH) prior to undergoing transcatheter aortic valve replacement (TAVR). NH level of care can be used to stabilize medical conditions, to provide rehabilitation services, or for long-term care services. Our primary objective is to determine whether NH utilization pre-TAVR can be used to stratify patients at risk for higher mortality and poor disposition outcomes at 30 and 365 days post-TAVR. METHODS: We conducted a retrospective cohort study among Medicare beneficiaries who spent ≥1 day in an NH 6 months before TAVR (2011-2019). The intensity of NH utilization was categorized as low users (1-30 days), medium users (31-89 days), long-stay NH residents (≥ 100 days, with no more than a 10-day gap in care), and high post-acute rehabilitation patients (≥90 days, with more than a 10-day gap in care). The probabilities of death and disposition were estimated using multinomial logistic regression, adjusting for age, sex, and race. RESULTS: Among 15,581 patients, 9908 (63.6%) were low users, 4312 (27.7%) were medium users, 663 (4.3%) were high post-acute care rehab users, and 698 (4.4%) were long-stay NH residents before TAVR. High post-acute care rehabilitation patients were more likely to have dementia, weight loss, falls, and extensive dependence of activities of daily living (ADLs) as compared with low NH users. Mortality was the greatest in high post-acute care rehab users: 5.5% at 30 days, and 36.4% at 365 days. In contrast, low NH users had similar mortality rates compared with long-stay NH residents: 4.8% versus 4.8% at 30 days, and 24.9% versus 27.0% at 365 days. CONCLUSION: Frequent bouts of post-acute rehabilitation before TAVR were associated with adverse outcomes, yet this metric may be helpful to determine which patients with severe AS could benefit from palliative and geriatric services.

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BACKGROUND: Major depressive disorder (MDD) is a disabling mental illness that can affect all aspects of daily life and is a leading cause of healthcare resource utilisation (HCRU). AIMS: We aimed to characterise patients with MDD with moderate-to-high-suicide-intent, compare their HCRU to patients with MDD without moderate-to-high-suicide-intent, and better understand their patient pathways. METHODS: This retrospective cohort study used data collected from primary care electronic health records from Clinical Practice Research Datalink (CPRD), linked to Hospital Episode Statistics, Mental Health Services Data Set, and Office for National Statistics in England. Adults diagnosed with ≥ 1 MDD diagnosis between 04/2007 and 11/2015 were categorised by suicide intent. RESULTS: 307,476 patients with MDD were included (294,259 patients without moderate-to-high-suicide-intent and 13,217 with moderate-to-high-suicide-intent). Patients with MDD with moderate-to-high-suicide-intent were younger on average (39.0 vs. 44.8 years) and included a lower percentage of females (58% vs. 65%) compared to patients without moderate-to-high-suicide-intent. HCRU was greater among patients with moderate-to-high-suicide-intent than patients without moderate-to-high-suicide-intent during the first follow-up year for general practitioner consultations (38.5 vs. 29.4), psychiatric outpatient visits (1.5 vs. 0.1), psychiatrist visits (3.6 vs. 0.3), emergency visits (1.5 vs. 0.3), and hospitalisations (86% vs. 26%). Overall, 56% of patients with moderate-to-high-suicide-intent had an antidepressant prescription within 30 days from the initial moderate-to-high-suicide-intent. CONCLUSIONS: Patients with MDD and moderate-to-high-suicide-intent were younger, included more males and incurred greater HCRU than those without moderate-to-high-suicide-intent. These results suggest a greater need for effective medical care and appropriate treatments for patients with moderate-to-high-suicide-intent, which could help reduce associated symptoms, mortality, and HCRU.

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As digital phenotyping, the capture of active and passive data from consumer devices such as smartphones, becomes more common, the need to properly process the data and derive replicable features from it has become paramount. Cortex is an open-source data processing pipeline for digital phenotyping data, optimized for use with the mindLAMP apps, which is used by nearly 100 research teams across the world. Cortex is designed to help teams (1) assess digital phenotyping data quality in real time, (2) derive replicable clinical features from the data, and (3) enable easy-to-share data visualizations. Cortex offers many options to work with digital phenotyping data, although some common approaches are likely of value to all teams using it. This paper highlights the reasoning, code, and example steps necessary to fully work with digital phenotyping data in a streamlined manner. Covering how to work with the data, assess its quality, derive features, and visualize findings, this paper is designed to offer the reader the knowledge and skills to apply toward analyzing any digital phenotyping data set. More specifically, the paper will teach the reader the ins and outs of the Cortex Python package. This includes background information on its interaction with the mindLAMP platform, some basic commands to learn what data can be pulled and how, and more advanced use of the package mixed with basic Python with the goal of creating a correlation matrix. After the tutorial, different use cases of Cortex are discussed, along with limitations. Toward highlighting clinical applications, this paper also provides 3 easy ways to implement examples of Cortex use in real-world settings. By understanding how to work with digital phenotyping data and providing ready-to-deploy code with Cortex, the paper aims to show how the new field of digital phenotyping can be both accessible to all and rigorous in methodology.

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Land degradation often results in poor soil quality in many parts of Ethiopia, including the study area. To address this issue and promote sustainable land management practices, various land use and management methods (LUMMs) have been implemented. However, little information is available regarding how these management practices influence overall soil quality dynamics of the study area. This study aimed at evaluating soil quality dynamics in the Urago micro-watershed, central highlands of Ethiopia, under major LUMMs: barren land (BL), grassland (GL), established farm boundary (EFB), restored degraded land (RDL), and stone-supported soil bund (SSB). Forty-five disturbed and fifteen undisturbed soil samples were collected from the ploughed soil layer (0-20 cm) of each LUMM and analysed for selected physicochemical properties to be used as indicators of soil quality. Principal component analysis and multiple correlation were used to select the minimum data set (MDS) to evaluate the overall soil quality index (SQI). The MDS included SOC, clay content, exchangeable Mg2+, and available P, which could replace other indicators for assessing the overall soil quality dynamics of the study watershed. The result showed notable variations in particle-size fractions, soil organic carbon (SOC), total nitrogen (TN), available P (av. P), and exchangeable Na+, K+, and Mg2+ levels among the LUMMs. RDL had higher sand and silt contents than SSB, whereas SSB had higher clay content compared to RDL, GL, and BL. GL, RDL, and EFB showed significantly higher levels of SOC, TN, and av. P, respectively, compared to other LUMMs. The obtained SQI showed that GL had the highest score (0.847), followed by SSB (0.703), RDL (0.701), EFB (0.644), and BL (0.628). This underscores the significance of stone-supported soil bund and restored degraded land as an efficient management method to enhance soil quality and agro-ecosystem through conserving soil and encouraging sustainable farming practices.

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BACKGROUND AND AIMS: Although minimally invasive hysterectomy offers advantages, abdominal hysterectomy remains the predominant surgical method. Creating a standardized dataset and establishing a hysterectomy registry system present opportunities for early interventions in reducing volume and selecting benign hysterectomy methods. This research aims to develop a dataset for designing benign hysterectomy registration system. METHODS: Between April and September 2020, a qualitative study was carried out to create a data set for enrolling patients who were candidate for hysterectomy. At this stage, the research team conducted an information needs assessment, relevant data element identification, registry software development, and field testing; Subsequently, a web-based application was designed. In June 2023the registry software was evaluated using data extracted from medical records of patients admitted at Al-Zahra Hospital in Tabriz, Iran. RESULTS: During two months, 40 patients with benign hysterectomy were successfully registered. The final dataset for the hysterectomy patient registry comprise 11 main groups, 27 subclasses, and a total of 91 Data elements. Mandatory data and essential reports were defined. Furthermore, a web-based registry system designed and evaluated based on data set and various scenarios. CONCLUSION: Creating a hysterectomy registration system is the initial stride toward identifying and registering hysterectomy candidate patients. this system capture information about the procedure techniques, and associated complications. In Iran, this registry can serve as a valuable resource for assessing the quality of care delivered and the distribution of clinical measures.

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Objective: Reports of financial exploitation have steadily increased among older adults. Few studies have examined neuropsychological profiles for individuals vulnerable to financial exploitation, and existing studies have focused on susceptibility to scams, one specific type of financial exploitation. The current study therefore examines whether a general measure of financial exploitation vulnerability is associated with neuropsychological performance in a community sample. Methods: A sample (n = 116) of adults aged 50 or older without dementia completed a laboratory visit that measures physical and psychological functioning and a neuropsychological assessment, the Uniform Data Set-3 (UDS-3) and California Verbal Learning Test-II. Results: After covarying for demographics, current medical problems, financial literacy, and a global cognition screen, financial exploitation vulnerability was negatively associated with scores on the Multilingual Naming Test, Craft Story Recall and Delayed Recall, California Verbal Learning Test-II Delayed Recall and Recognition Discriminability, Phonemic Fluency, and Trails B. Financial exploitation vulnerability was not associated with performance on Digit Span, Semantic Fluency, Benson Complex Figure Recall, or Trails A. Conclusions: Among older adults without dementia, individuals at higher risk for financial exploitation demonstrated worse verbal memory, confrontation naming, phonemic fluency, and set-shifting. These tests are generally sensitive to Default Mode Network functioning and Alzheimer's Disease neuropathology. Longitudinal studies in more impaired samples are warranted to further corroborate and elucidate these relationships.

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The National School Health Data Set: Every Student Counts! (ESC!) is NASN's data initiative focusing on building data capacity for school nurses, a uniform data set with standardized definitions, and promoting data infrastructure including school nurse access to electronic documentation, interoperability of educational systems and school health records, and build partnerships to increase data collection, storage, retrievable, and utilization. Each year since 2018, states have submitted data to NASN for inclusion in the National School Health Data Set. Participation is built on a tiered programing model to include school nurses at the school, state, and national level. Every state has identified a State Data Coordinator (SDC) who serves as a liaison to NASN to support ESC! but also provides support to school nurses in their state. This article provides an overview of the ESC! data initiative for the 2023-2024 school year, which includes the data from the 2022-2023 school year.

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For a better understanding of cadmium (Cd) accumulation over long time periods in cereals, Cd levels of the German wheat and rye harvest from 1975 to 2021 were analyzed. Overall, wheat had higher grain Cd concentrations than rye. Comparing mean values from different time periods showed that Cd levels in winter rye have stabilized, while Cd concentrations in winter wheat have decreased. Furthermore, Cd concentrations in almost all samples were below the newly introduced European Commission limits specifying the maximum permissible contaminant levels in foodstuffs (Cd in grains: rye 50 µg/kg FW; wheat 100 µg/kg FW). However, it is important to note that Cd is still ubiquitous in the German wheat and rye harvest. Although there has been a significant reduction in emissions and imissions for around 30 years, the extraordinarily long biological half-life and carcinogenicity of Cd still make it a relevant substance to food safety and human health.

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Background: Large language models (LLMs) have achieved great progress in natural language processing tasks and demonstrated the potential for use in clinical applications. Despite their capabilities, LLMs in the medical domain are prone to generating hallucinations (not fully reliable responses). Hallucinations in LLMs' responses create substantial risks, potentially threatening patients' physical safety. Thus, to perceive and prevent this safety risk, it is essential to evaluate LLMs in the medical domain and build a systematic evaluation. Objective: We developed a comprehensive evaluation system, MedGPTEval, composed of criteria, medical data sets in Chinese, and publicly available benchmarks. Methods: First, a set of evaluation criteria was designed based on a comprehensive literature review. Second, existing candidate criteria were optimized by using a Delphi method with 5 experts in medicine and engineering. Third, 3 clinical experts designed medical data sets to interact with LLMs. Finally, benchmarking experiments were conducted on the data sets. The responses generated by chatbots based on LLMs were recorded for blind evaluations by 5 licensed medical experts. The evaluation criteria that were obtained covered medical professional capabilities, social comprehensive capabilities, contextual capabilities, and computational robustness, with 16 detailed indicators. The medical data sets include 27 medical dialogues and 7 case reports in Chinese. Three chatbots were evaluated: ChatGPT by OpenAI; ERNIE Bot by Baidu, Inc; and Doctor PuJiang (Dr PJ) by Shanghai Artificial Intelligence Laboratory. Results: Dr PJ outperformed ChatGPT and ERNIE Bot in the multiple-turn medical dialogues and case report scenarios. Dr PJ also outperformed ChatGPT in the semantic consistency rate and complete error rate category, indicating better robustness. However, Dr PJ had slightly lower scores in medical professional capabilities compared with ChatGPT in the multiple-turn dialogue scenario. Conclusions: MedGPTEval provides comprehensive criteria to evaluate chatbots by LLMs in the medical domain, open-source data sets, and benchmarks assessing 3 LLMs. Experimental results demonstrate that Dr PJ outperforms ChatGPT and ERNIE Bot in social and professional contexts. Therefore, such an assessment system can be easily adopted by researchers in this community to augment an open-source data set.

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With the rapid extensive development of the Internet, users not only enjoy great convenience but also face numerous serious security problems. The increasing frequency of data breaches has made it clear that the network security situation is becoming increasingly urgent. In the realm of cybersecurity, intrusion detection plays a pivotal role in monitoring network attacks. However, the efficacy of existing solutions in detecting such intrusions remains suboptimal, perpetuating the security crisis. To address this challenge, we propose a sparse autoencoder-Bayesian optimization-convolutional neural network (SA-BO-CNN) system based on convolutional neural network (CNN). Firstly, to tackle the issue of data imbalance, we employ the SMOTE resampling function during system construction. Secondly, we enhance the system's feature extraction capabilities by incorporating SA. Finally, we leverage BO in conjunction with CNN to enhance system accuracy. Additionally, a multi-round iteration approach is adopted to further refine detection accuracy. Experimental findings demonstrate an impressive system accuracy of 98.36%. Comparative analyses underscore the superior detection rate of the SA-BO-CNN system.

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Color plays a pivotal role in product design, as it can evoke emotional responses from users. Understanding these emotional needs is crucial for effective brand image design. This paper introduces a novel approach, the Brand Image Design using Deep Multi-Scale Fusion Neural Network optimized with Cheetah Optimization Algorithm (BID-DMSFNN-COA), for classifying product color brand images as "Stylish" and "Natural". By leveraging deep learning techniques and optimization algorithms, the proposed method aims to enhance brand image accuracy and address existing challenges in product color trend forecasting research. Initially, data are collected from the Mnist Data Set. The data are then supplied into the pre-processing section. In the pre-processing segment, it removes the noise and enhances the input image utilizing master slave adaptive notch filter. The Deep Multi-Scale Fusion Neural Network optimized with cheetah optimization algorithm effectively classifies the product colour brand image as "Stylish" and "Natural". Implemented on the MATLAB platform, the BID-DMSFNN-COA technique achieves remarkable accuracy rates of 99 % for both "Natural" and "Stylish" classifications. In comparison, existing methods such as BID-GNN, BID-ANN, and BID-CNN yield lower accuracy rates ranging from 65 % to 85 % for "Stylish" and 65 %-70 % for "Natural" product color brand image design. The simulation outcomes reveal the superior performance of the BID-DMSFNN-COA technique across various metrics including accuracy, F-score, precision, recall, sensitivity, specificity, and ROC analysis. Notably, the proposed method consistently outperforms existing approaches, providing higher values across all evaluation criteria. These findings underscore the effectiveness of the BID-DMSFNN-COA technique in enhancing brand image design through accurate product color classification.