Solitary humpback whales manufacture bubble-nets as tools to increase prey intake.

Several animal species use tools for foraging; however, very few manufacture and/or modify those tools. Humpback whales, which manufacture bubble-net tools while foraging, are among these rare species. Using animal-borne tag and unoccupied aerial system technologies, we examine bubble-nets manufactured by solitary humpback whales (Megaptera novaeangliae) in Southeast Alaska while feeding on krill. We demonstrate that the nets consist of internally tangential rings and suggest that whales actively control the number of rings in a net, net size and depth and the horizontal spacing between neighbouring bubbles. We argue that whales regulate these net structural elements to increase per-lunge prey intake by, on average, sevenfold. We measured breath rate and swimming and lunge kinematics to show that the resulting increase in prey density does not increase energetic expenditure. Our results provide a novel insight into how bubble-net tools manufactured by solitary foraging humpback whales act to increase foraging efficiency.

Hygiene performance rating at farm level - an auditing scheme for evaluation of biosecurity measures' effect on prevalence of Campylobacter from selected broiler producers.

BACKGROUND: Preventing pathogens from entering the broiler premises is the main biosecurity measure at farm level. In conventional broiler production, chickens are kept indoors during the entire production period. Pathogens can enter the broiler-producing unit from sources such as water, equipment, personnel, insects, and rodents. The possible routes must be controlled, and corrective measures applied when necessary. The objective of this study was to (1) develop a hygiene protocol and test the scheme on 30 farms, and (2) compare the results to their Campylobacter-colonised status. A Hygiene Performance Rating protocol at farm level (HPR-F) was developed to systematically review the production to identify risk areas to biosecurity. The HPR-F consists of 13 categories with related questions. For each question, a score was given from 1 to 3, where 1 meant "acceptable", 2 was "potential for improvements", and 3 was "not acceptable". Scores for each question were multiplied with weight factors for hygienic impact and economic consequences describing whether the necessary improvement depends on a significant investment or is a cheap quick-fix and calculated into a percentage where 100% is perfect hygiene. The 30 farms in the study were selected from one county in Norway. The Campylobacter-results for each of the 30 farms in 2019-2021 were given according to rules in the Norwegian Action Plan against Campylobacter faecal sampling on-farm 3-6 days prior to slaughter. RESULTS: The overall results from the HPR-F showed that the general hygiene level was high in all farms. The mean total hygiene score was 82% and varied from 70 to 92%. The category Handling dead chicken had the highest hygiene score (93%), and Ventilation had the lowest score (55%). The HPR-F results were compared to the Campylobacter-status for the 30 farms: Campylobacter-negative flocks had slightly higher total scores than Campylobacter-positive flocks (P = 0.19). Among others, the category Outdoor area (vegetation close to the premises' walls) was identified as the most stable factor in relation to be colonised with Campylobacter. CONCLUSIONS: The HPR-F tested in this research trial provides a tool for veterinarians, advisors, and poultry farmers to improve biosecurity at farm level and enhance the preventive animal health initiatives.

Measuring Social Frailty: A Scoping Review of Available Scales and Tools.

BACKGROUND AND OBJECTIVES: Social frailty is an emerging concept characterized by state of vulnerability due to the lack or absence of social resources that enable health and wellbeing, particularly among older people. However, there is no consensus on how to accurately measure and assess social frailty, given the broad coverage of social dimensions affecting older individuals. This scoping review aimed to identify existing tools and scales used to measure social frailty in older people. RESEARCH DESIGN AND METHODS: A scoping review methodology was employed. Articles published between January 2014 and April 2024 were searched in six electronic databases: PubMed, PsycINFO, ProQuest, Scopus, SocIndex, and CINAHL. The scoping review followed a five-stage process by Arksey and O'Malley and adhered to the guidelines provided by PRISMA-ScR. RESULTS: Nine social frailty tools were identified across the 58 papers included in this review. The individual question item commonly used in the scales were classified into main categories based on their conceptual characteristics and intentions. The most common individual questionnaire constructs used to measure social frailty included financial status, social resources, social behaviours and activities, and sense of purpose. The reviewed tools varied in terms of their robustness and the process of scale development. DISCUSSION AND IMPLICATIONS: Our review suggested the development of a standardized, psychometrically tested, and accurate screening tool to screen social frailty status. An accurate social frailty assessment can inform the development of useful interventions, which also has implications in preventing the development of physical frailty.

Trans2express - de novo transcriptome assembly pipeline optimized for gene expression analysis.

BACKGROUND: As genomes of many eukaryotic species, especially plants, are large and complex, their de novo sequencing and assembly is still a difficult task despite progress in sequencing technologies. An alternative to genome assembly is the assembly of transcriptome, the set of RNA products of the expressed genes. While a bunch of de novo transcriptome assemblers exists, the challenges of transcriptomes (the existence of isoforms, the uneven expression levels across genes) complicates the generation of high-quality assemblies suitable for downstream analyses. RESULTS: We developed Trans2express - a web-based tool and a pipeline of de novo hybrid transcriptome assembly and postprocessing based on rnaSPAdes with a set of subsequent filtrations. The pipeline was tested on Arabidopsis thaliana cDNA sequencing data obtained using Illumina and Oxford Nanopore Technologies platforms and three non-model plant species. The comparison of structural characteristics of the transcriptome assembly with reference Arabidopsis genome revealed the high quality of assembled transcriptome with 86.1% of Arabidopsis expressed genes assembled as a single contig. We tested the applicability of the transcriptome assembly for gene expression analysis. For both Arabidopsis and non-model species the results showed high congruence of gene expression levels and sets of differentially expressed genes between analyses based on genome and based on the transcriptome assembly. CONCLUSIONS: We present Trans2express - a protocol for de novo hybrid transcriptome assembly aimed at recovering of a single transcript per gene. We expect this protocol to promote the characterization of transcriptomes and gene expression analysis in non-model plants and web-based tool to be of use to a wide range of plant biologists.

Carbon emission warning method for machine tool manufacturing process based on dynamic adaptive EWMA control chart.

Machine tools constitute the backbone of the industrial sector, representing the largest global inventory of equipment. The carbon emissions resulting from the production of each machine tool merit attention. Effective management of carbon emissions in the machine tool manufacturing process is crucial. This paper introduces a novel method for early carbon emission warnings in the machine tool manufacturing process, utilizing an adaptive dynamic exponentially weighted moving average (EWMA) approach. This method addresses the challenges in identifying and monitoring abnormal carbon emissions, emerging from uncertainties and dynamic correlations. Utilizing dynamic sampling techniques and adaptive principles, this method constructs an adaptive dynamic EWMA control chart. The EWMA control chart incorporates a multi-objective optimization design model, concentrating on carbon emissions in the machine tool manufacturing process, and incorporates statistical, economic, and environmental objectives. To mitigate slow convergence rates and enhance optimization accuracy in complex control chart multi-objective optimization algorithms, this study proposes an enhanced Harris hawks optimization (HHO) algorithm as the solving algorithm. Finally, the application of this method is demonstrated through the monitoring of carbon emissions in the manufacturing process of a five-axis machine tool (EOC), as a case study. The results validate the method's rapid responsiveness to abnormal carbon emissions, providing alerts. This further confirms the efficacy and feasibility of the proposed approach. Ultimately, this approach offers a viable strategy for fostering environmentally conscious and high-quality growth in the machine tool industry.

Developing the PIP-eco: An integrated genomic pipeline for identification and characterization of Escherichia coli pathotypes encompassing hybrid forms.

Pathogenic Escherichia coli (E. coli) strains are distinguished by their diverse virulence factors, which contribute to a wide spectrum of diseases. These pathogens evolve through the horizontal transfer of virulence factors, resulting in the emergence of hybrid pathotypes with complex and heterogeneous characteristics. Recognizing their profound impact on public health, this study introduces the PIP-eco pipeline, a comprehensive analytical tool designed for the precise identification and characterization of E. coli pathotypes. This PIP-eco pipeline advances beyond traditional molecular techniques by facilitating detailed analysis of both single and hybrid pathotypes. It integrates targeted marker gene analysis, virulence factor-based phylogenetic analysis, and pathogenicity islands (PAIs) profiling to elucidate the genetic diversity of E. coli pathotypes and support their accurate classification. This integrative approach enables PIP-eco to uncover connections among various E. coli pathotypes, highlight shared virulence factors, and provide insights into their evolutionary trajectories. By utilizing experimentally validated marker genes, the pipeline ensures robust identification of pathotypes, particularly those of hybrid pathotypes. Additionally, PAI analysis offers comprehensive genetic investigations, revealing strain-specific variations and potential virulence mechanisms. As a result, the PIP-eco pipeline emerges as a useful tool for dissecting the evolutionary dynamics of E. coli and characterizing complex pathotypes, addressing the critical need for accurate detection and understanding of hybrid pathotypes.

A Cross-Sectional Study on Bedside Abdominal Ultrasound Findings as a Diagnostic and Prognostic Tool in Dengue Fever in Manipal Hospital, Bengaluru, India.

Background Dengue fever poses a significant health burden globally, particularly in tropical and subtropical regions. Early diagnosis and effective management are crucial in reducing morbidity and mortality associated with the disease. Bedside abdominal ultrasound has emerged as a promising tool for assessing dengue patients, providing real-time imaging of abdominal organs, and aiding clinical decision-making. Materials and methods This cross-sectional study was conducted in 55 adult emergency departments of Manipal Hospital, Bengaluru, from March 2017 to March 2018. Adult patients presenting with signs and symptoms suggestive of dengue fever were included. Clinical data, laboratory investigations, and bedside abdominal ultrasound findings were systematically recorded and analyzed using appropriate statistical methods. Results Descriptive statistics revealed characteristic clinical measurements and symptom ratings observed in dengue fever patients. Frequency distributions highlighted common symptoms encountered, while statistical analyses demonstrated significant associations between ultrasonic parameters, disease severity, and outcomes. The study found notable correlations between ultrasonic findings and dengue severity levels, emphasizing the potential of bedside ultrasound as a diagnostic and prognostic tool. Conclusion Bedside abdominal ultrasound shows promise as a valuable adjunctive tool in assessing dengue fever patients. The significant associations between ultrasonic parameters and disease severity suggest its utility in risk stratification and guiding clinical management decisions. Incorporating bedside ultrasound into routine practice may improve patient care and outcomes in dengue fever management. Further research is warranted to validate these findings and explore additional bedside ultrasound applications in dengue fever diagnosis and prognosis.

Identification of Motivational Determinants for Telemedicine Use Among Patients With Rheumatoid Arthritis in Germany: Secondary Analysis of Data From a Nationwide Cross-Sectional Survey Study.

BACKGROUND: Previous studies have demonstrated telemedicine to be an effective tool to complement rheumatology care and address workforce shortage. With the COVID-19 outbreak, telemedicine experienced a massive upswing. An earlier analysis revealed that the motivation of patients with rheumatic and musculoskeletal diseases to use telemedicine is closely connected to their disease. It remains unclear which factors are associated with patients' motivation to use telemedicine in certain rheumatic and musculoskeletal diseases groups, such as rheumatoid arthritis (RA). OBJECTIVE: This study aims to identify factors that determine the willingness to try telemedicine among patients diagnosed with RA. METHODS: We conducted a secondary analysis of data from a German nationwide cross-sectional survey among patients with RA. Bayesian univariate logistic regression analysis was applied to the data to determine which factors were associated with willingness to try telemedicine. Predictor variables (covariates) studied individually included sociodemographic factors (eg, age, sex) and health characteristics (eg, health status). All the variables positively and negatively associated with willingness to try telemedicine in the univariate analyses were then considered for Bayesian model averaging analysis after a selection based on the variance inflation factor (≤ 2.5) to identify determinants of willingness to try telemedicine. RESULTS: Among 438 surveyed patients in the initial study, 210 were diagnosed with RA (47.9%). Among them, 146 (69.5%) answered either yes or no regarding willingness to try telemedicine and were included in the analysis. A total of 22 variables (22/55, 40%) were associated with willingness to try telemedicine (region of practical equivalence %≤5). A total of 9 determinant factors were identified using Bayesian model averaging analysis. Positive determinants included desiring telemedicine services provided by a rheumatologist (odds ratio [OR] 13.7, 95% CI 5.55-38.3), having prior knowledge of telemedicine (OR 2.91, 95% CI 1.46-6.28), residing in a town (OR 2.91, 95% CI 1.21-7.79) or city (OR 0.56, 95% CI 0.23-1.27), and perceiving one's health status as moderate (OR 1.87, 95% CI 0.94-3.63). Negative determinants included the lack of an electronic device (OR 0.1, 95% CI 0.01-0.62), absence of home internet access (OR 0.1, 95% CI 0.02-0.39), self-assessment of health status as bad (OR 0.44, 95% CI 0.21-0.89) or very bad (OR 0.47, 95% CI 0.06-2.06), and being aged between 60 and 69 years (OR 0.48, 95% CI 0.22-1.04) or older than 70 years (OR 0.38, 95% CI 0.16-0.85). CONCLUSIONS: The results suggest that some patients with RA will not have access to telemedicine without further support. Older patients, those not living in towns, those without adequate internet access, reporting a bad health status, and those not owning electronic devices might be excluded from the digital transformation in rheumatology and might not have access to adequate RA care. These patient groups certainly require support for the use of digital rheumatology care.

Evaluation of AI-Driven LabTest Checker for Diagnostic Accuracy and Safety: Prospective Cohort Study.

Background: In recent years, the implementation of artificial intelligence (AI) in health care is progressively transforming medical fields, with the use of clinical decision support systems (CDSSs) as a notable application. Laboratory tests are vital for accurate diagnoses, but their increasing reliance presents challenges. The need for effective strategies for managing laboratory test interpretation is evident from the millions of monthly searches on test results' significance. As the potential role of CDSSs in laboratory diagnostics gains significance, however, more research is needed to explore this area. Objective: The primary objective of our study was to assess the accuracy and safety of LabTest Checker (LTC), a CDSS designed to support medical diagnoses by analyzing both laboratory test results and patients' medical histories. Methods: This cohort study embraced a prospective data collection approach. A total of 101 patients aged ≥18 years, in stable condition, and requiring comprehensive diagnosis were enrolled. A panel of blood laboratory tests was conducted for each participant. Participants used LTC for test result interpretation. The accuracy and safety of the tool were assessed by comparing AI-generated suggestions to experienced doctor (consultant) recommendations, which are considered the gold standard. Results: The system achieved a 74.3% accuracy and 100% sensitivity for emergency safety and 92.3% sensitivity for urgent cases. It potentially reduced unnecessary medical visits by 41.6% (42/101) and achieved an 82.9% accuracy in identifying underlying pathologies. Conclusions: This study underscores the transformative potential of AI-based CDSSs in laboratory diagnostics, contributing to enhanced patient care, efficient health care systems, and improved medical outcomes. LTC's performance evaluation highlights the advancements in AI's role in laboratory medicine.

The Norwegian traumatic grief inventory-self report plus (TGI-SR+): a psychometric evaluation in traumatically bereaved people.

Background: Prolonged grief disorder (PGD) has been added to the ICD-11 and DSM-5-TR. The Traumatic Grief Inventory-Self Report Plus (TGI-SR+) assesses self-rated PGD intensity as defined in ICD-11 and DSM-5-TR. The TGI-SR + is available in multiple languages, but has not been validated yet in Norwegian.Objective: The aim of this study was to evaluate the psychometric properties of the Norwegian TGI-SR + .Method: Bereaved adults (N = 307) whose child or sibling died ≥6 months ago due to a sudden or violent loss completed the TGI-SR + and measures for posttraumatic stress, depression, and precursor PGD symptoms. We examined the factor structure and internal consistency of the ICD-11 and DSM-5-TR PGD items. Convergent validity and known-groups validity was evaluated. Probable PGD cases, pair-wise agreement between diagnostic scoring rules for both PGD criteria-sets, and cut-off scores were calculated.Results: The 1-factor model for ICD-11 and DSM-5-TR PGD showed the best fit and demonstrated good internal consistency. Convergent validity was supported by strong associations between summed ICD-11 and DSM-5-TR PGD scores and summed posttraumatic stress, depression, and precursor prolonged grief scores. Known-groups validity was supported by PGD intensity being related to educational level and time since loss. The perfect pair-wise agreement was reached using the ICD-11 and DSM-5-TR PGD diagnostic scoring rules. The optimal cut-off score for detecting probable PGD cases, when summing all TGI-SR + items, was ≥73.Conclusions: The Norwegian TGI-SR + seems a valid and reliable instrument to assess ICD-11 and DSM-5-TR PGD intensity after losing a child or sibling under traumatic circumstances.

The TGI-SR + is a self-report instrument assessing ICD-11 and DSM-5-TR prolonged grief disorder intensity.The Norwegian TGI-SR + seems to be a valid and reliable instrument to assess prolonged grief disorder intensity.Optimal cut-off for detecting probable prolonged grief disorder cases is ≥73 in this traumatically bereaved sample of parents and siblings.

Assessing family relationships through drawing: the Family Life Space.

The Family Life Space (FLS) is a graphic instrument that may aid the relational assessment of families. This interactive instrument involves all members of the family in a joint task, that of collectively making a drawing of their own family system and it allows the gathering of information related to the overall family organization. The FLS was originally conceived by Danuta Mostwin in the early 70s and used as an instrument for clinical intervention. After having been applied to several contexts, the present contribution aims at presenting the key indicators to use the instrument as a tool for assessing family relations. Specifically, the characteristics of the instrument that allow the gathering of relevant information at the individual, relational, and family level will be outlined. For each of these levels, the data regarding the quantity and quality of the elements in the drawing that define the family space (i.e., the number and graphic quality of the actual elements in the drawing) as well the quantity and quality of the relationships among family members and with their community at large (i.e., the number and type of lines connecting the various elements in the drawing) will be presented. The instrument can therefore provide useful insights on the following constructs: quality of life, power dynamics within the family, feelings of belonging, closeness and/or distance as well as conflict or acknowledgement between family members and the overall attitude family members have toward their context and the critical events they had to face. The application and complete potential of the instrument are further elaborated upon through the presentation of a clinical case. This case not only aids in comprehending the tool's usage but also enables the collection of psychological information about the family and provides a clinical interpretation of family relationships.

Research progress in stroke risk perception assessment tool.

BACKGROUND: One of the main illnesses in the globe that causes impairment and death in people is stroke. In the globe today, it ranks as the second leading cause of death and the leading cause of death in China. OBJECTIVE: This paper analyses into the critical role of risk perception in developing individual awareness of stroke risk and encouraging proactive preventive health behaviors, essential for effective primary stroke prevention strategies and reduced stroke incidence. It discusses the concept of risk perception, the content and dimensions of global stroke assessment tools, and their application status, aiming to provide insights for their development and intervention research. METHODS: Risk perception encompasses subjective assessments of stroke likelihood and severity, influenced by personal experiences, knowledge of risk factors, beliefs about prevention effectiveness, and emotional responses. Global stroke assessment tools, like the Framingham Stroke Risk Score and CHA2DS2-VASc Score, evaluate stroke risk based on factors such as age, gender, blood pressure, and cholesterol levels. In order to improve risk perception and proactive health management and lower the burden of strokes, the paper assesses the advantages and disadvantages of these tools and makes recommendations for improving accessibility, customizing interventions, running educational campaigns, promoting multidisciplinary collaboration, and integrating technology. RESULTS: By combining the research tools of stroke risk perception, it is found that the evaluation tools are mostly single-dimensional evaluation tools centered on the two dimensions of onset possibility and susceptibility. CONCLUSION: Some scholars have developed multi-dimensional evaluation tools, but the evaluation population is relatively limited, and the evaluation system lacks comprehensiveness and systematization.

How children come to (not) detect and apply multiple functions for objects: Rethinking perseveration and functional fixedness.

Although humans acquire sophisticated and flexible tool-use skills rapidly throughout childhood, young children and adults still show difficulties using the same object for different functions, manifesting in, for example, perseveration or functional fixedness. This paper presents a novel model proposing bottom-up processes taking place during the acquisition of tool-use abilities through active interaction with objects, resulting in two kinds of cognitive representations of an object: a lower-level, action-centered representation and a higher-level, purpose-centered one. In situations requiring the use of an object to attain a goal, the purpose-centered representation is activated quickly, allowing for an immediate detection of suitable tools. In contrast, activation of the action-centered representation is slow and effortful, but comes with the advantage of offering wide-ranging information about the object's features and how they can be applied. This differential availability and activation of action-centered versus purpose-centered representations also contributes to a deeper understanding of the cognitive mechanisms underlying perseveration or functional fixedness during multifunctional tool use. When applied to the teaching and acquisition of tool use, the model indicates that the form in which object-related information is provided determines which of the two object representations is fostered, thereby either facilitating or complicating the flexible application of an object as a tool for different functions.

The extracellular matrix in solid tumor immunotherapy.

The extracellular matrix (ECM) of solid tumors impacts the antitumor activities of CD8+ T and natural killer (NK) cells in a variety of ways. Cell motility is restricted by the tumor ECM which creates physical barriers. The tumor ECM directly alter the phenotypes and functions of cytotoxic lymphocytes, and indirectly influences immunological synapse-mediated interactions between cytotoxic lymphocytes and cancer cells. Therefore, strategies to improve solid tumor immunotherapy should be established by considering complex ternary interactions between cytotoxic lymphocytes, cancer cells, and the tumor ECM. Novel bioengineering tools approximating key characteristics of the tumor ECM, such as in vitro reconstituted 3D ECMs and microfluidics are valuable from a fundamental study viewpoint and from a translational perspective, aiming to enable systematic screening approaches.

A baseline monitoring of radiological sediment quality and associated risk assessment in coastal ecosystems of the Republic of Congo.

This study presents the first data on levels of natural radioactive elements in sediments from coastal ecosystems of the Republic of Congo. Sediment samples from five coastal sites were collected and analyzed by high-resolution gamma spectrometry for determination of activities of long-lived gamma-emitting radionuclides (234Th, 238U, 226Ra, 210Pb, 228Th, 228Ra, and 40 K). The specific activities were of the same order of magnitude as those measured in sediments of most countries neighboring the Republic of Congo. However, variations in activities were observed from one site to another and also from one sampling point to another within the same site without exceeding the global average reference values. It can be assumed, therefore, that no significant anthropogenic impact is perceptible in the study area. The most commonly used radiological hazard parameters, based mainly on 238U, 232Th, and 40 K activities, were assessed and the ERICA tool was applied to quantify the radiation exposure burden to human and biota resulting from radionuclides in sediments. Besides being useful for future monitoring efforts, the data produced in this work could be important for the worldwide database on radioactivity in the oceans and seas (MARIS) since no data are available in the Congolese marine environment.

Development and validation of a tool to assess underlying factors of iron-rich food consumption among pregnant women.

Anaemia among pregnant women remains a public health concern globally. One major cause of this persistent problem is iron deficiency, which may be the result of limited iron intake in the diet. Using the extended version of the theory of planned behaviour (eTPB), this study aims to develop and validate a questionnaire assessing psychosocial and environmental factors that could influence the consumption of iron-rich foods (IRFs) among Senegalese pregnant women. A three-step procedure was used. Six focus group discussions (FGDs) were held with 10 pregnant women each from a different region to identify salient beliefs related to each of the four constructs of the eTPB using a structured guide. Information from FGDs was used to develop a questionnaire, which was administered to the first group (n = 200) of pregnant women. Principal component analyses and exploratory factorial analyses were performed on the first set of data to identify latent factors for each construct namely the attitude, subjective norm and perceived behavioural control. A revised and shorter version of the questionnaire was administered to a second sample of pregnant women (n = 226) and confirmatory factorial analyses were conducted using this second set of data. Hancock and Muller's H reliability index was computed on the final model. The final questionnaire included 44 items. Most criteria for fit indices were met and H values were satisfactory. This study proposes a tool that could be used to explore determinants of the consumption of IRF among pregnant women. Further validation is still warranted in other contexts.

A decision support tool to help identify blue carbon sites for restoration.

Blue carbon ecosystems (BCEs), such as mangroves, saltmarshes, and seagrasses, are important nature-based solutions for climate change mitigation and adaptation but are threatened by degradation. Effective BCE restoration requires strategic planning and site selection to optimise outcomes. We developed a Geographic Information System (GIS)-based multi-criteria decision support tool to identify suitable areas for BCE restoration along the 2512 km-long coastline of Victoria, Australia. High-resolution spatial data on BCE distribution, coastal geomorphology, hydrodynamics, and land tenure were integrated into a flexible spatial model that distinguishes between passive and active restoration suitability. The tool was applied to identify high-priority locations for mangrove, saltmarsh, and seagrass restoration across different scenarios. Results indicate substantial potential for BCE restoration in Victoria, with 33,253 ha of suitable area identified, mostly (>97%) on public land, which aligned with the selection criteria used in the tool. Restoration opportunities are concentrated in bays and estuaries where historical losses have been significant. The mapped outputs provide a decision-support framework for regional restoration planning, while the tool itself can be adapted to other geographies. By integrating multiple spatial criteria and distinguishing between passive and active restoration, our approach offers a new method for targeting BCE restoration and informing resource allocation. The identified restoration potential will also require collaboration with coastal managers and communities, and consideration of socio-economic factors. With further refinements, such as incorporating multi-criteria decision analysis techniques, GIS-based tools can help catalyse strategic blue carbon investments and contribute to climate change mitigation and adaptation goals at different spatial scales. This study highlights the value of spatial identification for BCE restoration and provides a transferable framework for other regions.

Mapping ultra-processed foods (UPFs) in India: a formative research study.

BACKGROUND: Increased consumption of ultra-processed foods (UPFs) which have additives such as artificial colours, flavours and are usually high in salt, sugar, fats and specific preservatives, are associated with diet-related non-communicable diseases (NCDs). In India, there are no standard criteria for identifying UPFs using a classification system based on extent and purpose of industrial processing. Scientific literature on dietary intake of foods among Indian consumers classifies foods as unhealthy based on presence of excessive amounts of specific nutrients which makes it difficult to distinguish UPFs from other commercially available processed foods. METHODS: A literature review followed by an online grocery retailer scan for food label reading was conducted to map the types of UPFs in Indian food market and scrutinize their ingredient list for the presence of ultra-processed ingredients. All UPFs identified were randomly listed and then grouped into categories, followed by saliency analysis to understand preferred UPFs by consumers. Indian UPF categories were then finalized to inform a UPF screener. RESULTS: A lack of application of a uniform definition for UPFs in India was observed; hence descriptors such as junk-foods, fast-foods, ready-to-eat foods, instant-foods, processed-foods, packaged-foods, high-fat-sugar-and-salt foods were used for denoting UPFs. After initial scanning of such foods reported in literature based on standard definition of UPFs, an online grocery retailer scan of food labels for 375 brands (atleast 3 brands for each food item) confirmed 81 food items as UPFs. A range of packaged traditional recipes were also found to have UPF ingredients. Twenty three categories of UPFs were then developed and subjected to saliency analysis. Breads, chips and sugar-sweetened beverages (e.g. sodas and cold-drinks) were the most preferred UPFs while frozen ready-to-eat/cook foods (e.g. chicken nuggets and frozen kebabs) were least preferred. CONCLUSION: India needs to systematically apply a food classification system and define Indian food categories based on the level of industrial processing. Mapping of UPFs is the first step towards development of a quick screener that would generate UPF consumption data to inform clear policy guidelines and regulations around UPFs and address their impact on NCDs.

Automated Behavioral Coding to Enhance the Effectiveness of Motivational Interviewing in a Chat-Based Suicide Prevention Helpline: Secondary Analysis of a Clinical Trial.

BACKGROUND: With the rise of computer science and artificial intelligence, analyzing large data sets promises enormous potential in gaining insights for developing and improving evidence-based health interventions. One such intervention is the counseling strategy motivational interviewing (MI), which has been found effective in improving a wide range of health-related behaviors. Despite the simplicity of its principles, MI can be a challenging skill to learn and requires expertise to apply effectively. OBJECTIVE: This study aims to investigate the performance of artificial intelligence models in classifying MI behavior and explore the feasibility of using these models in online helplines for mental health as an automated support tool for counselors in clinical practice. METHODS: We used a coded data set of 253 MI counseling chat sessions from the 113 Suicide Prevention helpline. With 23,982 messages coded with the MI Sequential Code for Observing Process Exchanges codebook, we trained and evaluated 4 machine learning models and 1 deep learning model to classify client- and counselor MI behavior based on language use. RESULTS: The deep learning model BERTje outperformed all machine learning models, accurately predicting counselor behavior (accuracy=0.72, area under the curve [AUC]=0.95, Cohen κ=0.69). It differentiated MI congruent and incongruent counselor behavior (AUC=0.92, κ=0.65) and evocative and nonevocative language (AUC=0.92, κ=0.66). For client behavior, the model achieved an accuracy of 0.70 (AUC=0.89, κ=0.55). The model's interpretable predictions discerned client change talk and sustain talk, counselor affirmations, and reflection types, facilitating valuable counselor feedback. CONCLUSIONS: The results of this study demonstrate that artificial intelligence techniques can accurately classify MI behavior, indicating their potential as a valuable tool for enhancing MI proficiency in online helplines for mental health. Provided that the data set size is sufficiently large with enough training samples for each behavioral code, these methods can be trained and applied to other domains and languages, offering a scalable and cost-effective way to evaluate MI adherence, accelerate behavioral coding, and provide therapists with personalized, quick, and objective feedback.

Abated crowding by fast-tracking the Throughput component of the ED for patients in no need of hospitalization with competency managed personnel.

BACKGROUND: Emergency department (ED) crowding is a major patient safety concern and has a negative impact on healthcare systems and healthcare providers. We hypothesized that it would be feasible to control crowding by employing a multifaceted approach consisting of systematically fast-tracking patients who are mostly not in need of a hospital stay as assessed by an initial nurse and treated by decision competent physicians. METHODS: Data from 120,901 patients registered in a secondary care ED from the 4tth quarter of 2021 to the 1st quarter of 2024 was drawn from the electronic health record's data warehouse using the SAP Web Intelligence tool and processed in the Python programming language. Crowding was compared before and after ED transformation from a uniform department into a high flow (α) and a low flow (ß) section with patient placement in gurneys/chairs or beds, respectively. Patients putatively not in need of hospitalization were identified by nurse, placed in in the α setting and assessed and treated by decision competent physicians. Incidence of crowding, number of patients admitted per day and readmittances within 72 h following ED admission before and after changes were determined. Values are number of patients, mean ± SEM and mean differences with 95% CIs. Statistical significance was ascertained using Student's two tailed t-test for unpaired values. RESULTS: Before and after ED changes crowding of 130% amounted to 123.8 h and 19.3 h in the latter. This is a difference of -104.6 ± 23.9 h with a 95% CI of -159.9 to -49.3, Δ% -84 (p = 0.002). There was the same amount of patients / day amounting to 135.8 and 133.5 patients / day Δ% = -1.7 patients 95% CI -6.3 to 1.6 (p = 0.21). There was no change in readmittances within 72 h before and after changes amounting to 9.0% versus 9.5%, Δ% = 0.5, 95%, CI -0.007 to 1.0 (p > 0.052). CONCLUSION: It appears feasible to abate crowding with unchanged patient admission and without an increase in readmittances by fast-track assessment and treatment of patients who are not in need of hospitalization.