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Background: The increasing prevalence of electronic health records (EHRs) in healthcare systems globally has underscored the importance of data quality for clinical decision-making and research, particularly in obstetrics. High-quality data is vital for an accurate representation of patient populations and to avoid erroneous healthcare decisions. However, existing studies have highlighted significant challenges in EHR data quality, necessitating innovative tools and methodologies for effective data quality assessment and improvement. Objective: This article addresses the critical need for data quality evaluation in obstetrics by developing a novel tool. The tool utilizes Health Level 7 (HL7) Fast Healthcare Interoperable Resources (FHIR) standards in conjunction with Bayesian Networks and expert rules, offering a novel approach to assessing data quality in real-world obstetrics data. Methods: A harmonized framework focusing on completeness, plausibility, and conformance underpins our methodology. We employed Bayesian networks for advanced probabilistic modeling, integrated outlier detection methods, and a rule-based system grounded in domain-specific knowledge. The development and validation of the tool were based on obstetrics data from 9 Portuguese hospitals, spanning the years 2019-2020. Results: The developed tool demonstrated strong potential for identifying data quality issues in obstetrics EHRs. Bayesian networks used in the tool showed high performance for various features with area under the receiver operating characteristic curve (AUROC) between 75% and 97%. The tool's infrastructure and interoperable format as a FHIR Application Programming Interface (API) enables a possible deployment of a real-time data quality assessment in obstetrics settings. Our initial assessments show promised, even when compared with physicians' assessment of real records, the tool can reach AUROC of 88%, depending on the threshold defined. Discussion: Our results also show that obstetrics clinical records are difficult to assess in terms of quality and assessments like ours could benefit from more categorical approaches of ranking between bad and good quality. Conclusion: This study contributes significantly to the field of EHR data quality assessment, with a specific focus on obstetrics. The combination of HL7-FHIR interoperability, machine learning techniques, and expert knowledge presents a robust, adaptable solution to the challenges of healthcare data quality. Future research should explore tailored data quality evaluations for different healthcare contexts, as well as further validation of the tool capabilities, enhancing the tool's utility across diverse medical domains.
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Introduction: Leakage of orogastric secretions past the cuff of a tracheal tube is a contributory factor in ventilator-associated pneumonia. Current bench test methods specified in the International Standard for Anaesthetic and Respiratory Equipment (EN ISO 5361:2023) to test cuff leakage involve using a glass or plastic rigid cylinder model of the trachea. There is a need for more realistic models to inform cuff leakage. Methods: We used human computerised tomography data and additive manufacturing (3D printing), combined with casting techniques to fabricate a bio-inspired synthetic tracheal model with analogous tissue characteristics. We conducted cuff leakage tests according to EN ISO 5361:2023 and compared results for high-volume low-pressure polyvinyl chloride and polyurethane cuffs between the rigid cylinder trachea with our bio-inspired model. Results: The tracheal model demonstrated close agreement with published tracheal tissue hardness for cartilaginous and membranous soft tissues. For high-volume low-pressure polyvinyl chloride cuffs the leakage rate was >50% lower in the bio-inspired tracheal model compared with the rigid cylinder model (151 [8] vs 261 [11] ml h-1). For high-volume low-pressure polyurethane cuffs, much lower leakage rates were observed than polyvinyl chloride cuffs in both models with leakage rates higher for the bio-inspired trachea model (0.1 [0.2] vs 0 [0] ml h-1). Conclusion: A reproducible tracheal model that incorporates the mechanical properties of the human trachea can be manufactured from segmented CT images and additive manufactured moulds, providing a useful tool to inform future cuff development, leakage testing for industrial applications, and clinical decision-making. There are differences between cuff leakage rates between the bio-inspired model and the rigid cylinder recommended in EN ISO 5361:2023. The bio-inspired model could lead to more accurate and realistic cuff leakage rate testing which would support manufacturers in refining their designs. Clinicians would then be able to choose better tracheal tubes based on the outcomes of this testing.
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BACKGROUND: Stroke management requires a coordinated strategy, adhering to clinical pathways (CP) and value-based healthcare (VBHC) principles from onset to rehabilitation. However, the discrepancies between these pathways and actual patient experiences highlight the need for ongoing monitoring and addressing interoperability issues across multiple institutions in stroke care. To address this, the Fast Healthcare Interoperability Resource (FHIR) Implementation Guide (IG) standardizes the information exchange among these systems, considering a specific context of use. OBJECTIVE: Develop an FHIR IG for stroke care rooted in established stroke CP and VBHC principles. METHOD: We represented the stroke patient journey by considering the core stroke CP, the International Consortium for Health Outcomes Measurement (ICHOM) dataset for stroke, and a Brazilian case study using the Business Process Model and Notation (BPMN). Next, we developed a data dictionary that aligns variables with existing FHIR resources and adapts profiling from the Brazilian National Health Data Network (BNHDN). RESULTS: Our BPMN model encompassed three critical phases that represent the entire patient journey from symptom onset to rehabilitation. The stroke data dictionary included 81 variables, which were expressed as questionnaires, profiles, and extensions. The FHIR IG comprised nine pages: Home, Stroke-CP, Data Dictionary, FHIR, ICHOM, Artifacts, Examples, Downloads, and Security. We developed 96 artifacts, including 7 questionnaires, 27 profiles with corresponding example instances, 3 extensions, 18 value sets, and 14 code systems pertinent to ICHOM outcome measures. CONCLUSION: The FHIR IG for stroke in this study represents a significant advancement in healthcare interoperability, streamlining the tracking of patient outcomes for quality enhancement, facilitating informed treatment choices, and enabling the development of dashboards to promote collaborative excellence in patient care.
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Vías Clínicas , Accidente Cerebrovascular , Humanos , Accidente Cerebrovascular/terapia , Brasil , Interoperabilidad de la Información en Salud , Registros Electrónicos de Salud , Atención Médica Basada en ValorRESUMEN
Adaptive laboratory evolution (ALE) is a powerful tool for enhancing the fitness of cell lines in specific applications, including recombinant protein production. Through adaptation to nonstandard culture conditions, cells can develop specific traits that make them high producers. Despite being widely used for microorganisms and, to lesser extent, for mammalian cells, ALE has been poorly leveraged for insect cells. Here, we describe a method for adapting insect High Five and Sf9 cells to nonstandard culture conditions via an ALE approach. Aiming to demonstrate the potential of ALE to improve productivity of insect cells, two case studies are demonstrated. In the first, we adapted insect High Five cells from their standard pH (6.2) to neutral pH (7.0); this adaptation allowed to improve production of influenza virus-like particles (VLPs) by threefold, using the transient baculovirus expression vector system. In the second, we adapted insect Sf9 cells from their standard culture temperature (27 °C) to hypothermic growth (22 °C); this adaptation allowed to improve production of influenza VLPs by sixfold, using stable cell lines. These examples demonstrate the potential of ALE for enhancing productivity within distinct insect cell hosts and expression systems by manipulating different culture conditions.
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Proteínas Recombinantes , Animales , Proteínas Recombinantes/genética , Proteínas Recombinantes/metabolismo , Línea Celular , Células Sf9 , Baculoviridae/genética , Técnicas de Cultivo de Célula/métodos , Insectos/genética , Insectos/citología , Evolución Molecular Dirigida/métodos , Concentración de Iones de Hidrógeno , TemperaturaRESUMEN
This study focused on comparing distributed learning models with centralized and local models, assessing their efficacy in predicting specific delivery and patient-related outcomes in obstetrics using real-world data. The predictions focus on key moments in the obstetric care process, including discharge and various stages of hospitalization. Our analysis: using 6 different machine learning methods like Decision Trees, Bayesian methods, Stochastic Gradient Descent, K-nearest neighbors, AdaBoost, and Multi-layer Perceptron and 19 different variables with various distributions and types, revealed that distributed models were at least equal, and often superior, to centralized versions and local versions. We also describe thoroughly the preprocessing stage in order to help others implement this method in real-world scenarios. The preprocessing steps included cleaning and harmonizing missing values, handling missing data and encoding categorical variables with multisite logic. Even though the type of machine learning model and the distribution of the outcome variable can impact the result, we reached results of 66% being superior to the centralized and local counterpart and 77% being better than the centralized with AdaBoost. Our experiments also shed light in the preprocessing steps required to implement distributed models in a real-world scenario. Our results advocate for distributed learning as a promising tool for applying machine learning in clinical settings, particularly when privacy and data security are paramount, thus offering a robust solution for privacy-concerned clinical applications.
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Aprendizaje Automático , Obstetricia , Humanos , Femenino , Embarazo , Teorema de Bayes , Árboles de DecisiónRESUMEN
BACKGROUND: The literature is consensual regarding the academic community exhibiting higher levels of mental disorder prevalence than the general population. The potential of digital mental health apps for improving access to resources to cope with these issues is ample. However, studies have yet to be performed in Portugal on individuals' attitudes and perceptions toward digital mental health applications or their preferences and decision drivers on obtaining mental health care, self-assessment, or treatment. OBJECTIVE: This study aims to understand the determinants of digital mental health applications use in the Portuguese academic community of Porto, along with potential adoption barriers and enablers. METHODS: A cross-sectional, web-based survey was delivered via dynamic email to the University of Porto's academic community. Data collection occurred between September 20 and October 20, 2022. We used structural equation modeling to build three models, replicating a peer-reviewed and published study and producing a newly full mediation model shaped by the collected data. We tested the relationships between use of digital mental health apps and perceived stress, perceived need to seek help for mental health, perceived stigma, past use of mental health services, privacy concerns, and social influence. RESULTS: Of the 539 participants, 169 (31.4%) reported having used digital mental health apps. Perceived stress and a latent variable, comprising perceptions of mental health problems and coping strategies, were positively associated with mental health app use, while privacy concerns regarding one's information being accessible to others were negatively associated. Perceived stigma, need to seek help, and close relationships did not have a statistically significant direct effect. CONCLUSIONS: These findings can inform product and policy development of new, better-targeted digital mental health app interventions, with implications for researchers and academia, industry, and policymakers. Our study concludes that, to maximize adherence to these apps, they should have low to no financial charges, demonstrate evidence of their helpfulness and focus on the timely delivery of care. We also conclude that to foster digital mental health app use, there is a need to improve mental health literacy, namely regarding self-awareness of one's conditions, acceptable stress levels, and overall behavior towards mental health. TRIAL REGISTRATION: RR2-10.2196/41040.
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Salud Mental , Aplicaciones Móviles , Humanos , Portugal , Estudios Transversales , Encuestas y Cuestionarios , InternetRESUMEN
BACKGROUND: The escalating prevalence of cesarean delivery globally poses significant health impacts on mothers and newborns. Despite this trend, the underlying reasons for increased cesarean delivery rates, which have risen to 36.3% in Portugal as of 2020, remain unclear. This study delves into these issues within the Portuguese health care context, where national efforts are underway to reduce cesarean delivery occurrences. OBJECTIVE: This paper aims to introduce a machine learning, algorithm-based support system designed to assist clinical teams in identifying potentially unnecessary cesarean deliveries. Key objectives include developing clinical decision support systems for cesarean deliveries using interoperability standards, identifying predictive factors influencing delivery type, assessing the economic impact of implementing this tool, and comparing system outputs with clinicians' decisions. METHODS: This study used retrospective data collected from 9 public Portuguese hospitals, encompassing maternal and fetal data and delivery methods from 2019 to 2020. We used various machine learning algorithms for model development, with light gradient-boosting machine (LightGBM) selected for deployment due to its efficiency. The model's performance was compared with clinician assessments through questionnaires. Additionally, an economic simulation was conducted to evaluate the financial impact on Portuguese public hospitals. RESULTS: The deployed model, based on LightGBM, achieved an area under the receiver operating characteristic curve of 88%. In the trial deployment phase at a single hospital, 3.8% (123/3231) of cases triggered alarms for potentially unnecessary cesarean deliveries. Financial simulation results indicated potential benefits for 30% (15/48) of Portuguese public hospitals with the implementation of our tool. However, this study acknowledges biases in the model, such as combining different vaginal delivery types and focusing on potentially unwarranted cesarean deliveries. CONCLUSIONS: This study presents a promising system capable of identifying potentially incorrect cesarean delivery decisions, with potentially positive implications for medical practice and health care economics. However, it also highlights the challenges and considerations necessary for real-world application, including further evaluation of clinical decision-making impacts and understanding the diverse reasons behind delivery type choices. This study underscores the need for careful implementation and further robust analysis to realize the full potential and real-world applicability of such clinical support systems.
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Mammalian Notch signaling occurs when the binding of Delta or Jagged to Notch stimulates the proteolytic release of the Notch intracellular domain (NICD), which enters the nucleus to control target gene expression. To determine the temporal dynamics of events associated with Notch signaling under native conditions, we fluorescently tagged Notch and Delta at their endogenous genomic loci and visualized them upon pairing of receiver (Notch) and sender (Delta) cells as a function of time after cell contact. At contact sites, Notch and Delta immediately accumulated at 1:1 stoichiometry in synapses, which resolved by 15-20 min after contact. Synapse formation preceded the entrance of the Notch extracellular domain into the sender cell and accumulation of NICD in the nucleus of the receiver cell, which approached a maximum after â¼45 min and was prevented by chemical and genetic inhibitors of signaling. These findings directly link Notch-Delta synapse dynamics to NICD production with spatiotemporal precision.
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Núcleo Celular , Receptores Notch , Transducción de Señal , Sinapsis , Humanos , Núcleo Celular/metabolismo , Receptores Notch/metabolismo , Sinapsis/metabolismo , Proteínas de la Membrana/metabolismo , Proteínas de la Membrana/genética , Dominios Proteicos , Péptidos y Proteínas de Señalización Intracelular/metabolismo , Péptidos y Proteínas de Señalización Intracelular/genéticaRESUMEN
Background: Patients with end-stage renal disease (ESRD) undergoing haemodialysis (HD) are associated with low quality of life (QoL) and high disease-related symptoms. The patient self-report instruments can assess the burden of physical and psychological symptoms. The Dialysis Symptom Index (DSI) is a specific instrument to evaluate the multidimensional reported symptoms by patients undergoing HD. Aims: To translate, validate and assess the psychometric properties of the Portuguese version of the DSI. Methods: A cross-cultural adaptation process from English to Portuguese and a validation study (n = 156) were conducted. Reliability, validity and responsiveness were assessed. Results: The patients' most reported symptoms were, nausea (n = 86, 55%), muscle cramps (n = 92, 59%), feeling tired or lack of energy (n = 92, 59%), bone or joint pain (n = 88, 56%) and trouble staying asleep (n = 95, 60%). Cronbach's alpha of the DSI was 0.87, and intraclass correlation coefficient was 0.868 (95%CI 0.836-0.896). The smallest detectable change was 28.32. Conclusion: The Portuguese DSI demonstrates excellent psychometric properties for assessing HD patients' reported symptoms. It highlights symptom severity and impact, providing valuable insights for healthcare practitioners. Nurses can use the DSI to tailor interventions and enhance patient-centred care.
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BACKGROUND: The integration of artificial intelligence (AI) technologies, such as ChatGPT, in the educational landscape has the potential to enhance the learning experience of medical informatics students and prepare them for using AI in professional settings. The incorporation of AI in classes aims to develop critical thinking by encouraging students to interact with ChatGPT and critically analyze the responses generated by the chatbot. This approach also helps students develop important skills in the field of biomedical and health informatics to enhance their interaction with AI tools. OBJECTIVE: The aim of the study is to explore the perceptions of students regarding the use of ChatGPT as a learning tool in their educational context and provide professors with examples of prompts for incorporating ChatGPT into their teaching and learning activities, thereby enhancing the educational experience for students in medical informatics courses. METHODS: This study used a mixed methods approach to gain insights from students regarding the use of ChatGPT in education. To accomplish this, a structured questionnaire was applied to evaluate students' familiarity with ChatGPT, gauge their perceptions of its use, and understand their attitudes toward its use in academic and learning tasks. Learning outcomes of 2 courses were analyzed to propose ChatGPT's incorporation in master's programs in medicine and medical informatics. RESULTS: The majority of students expressed satisfaction with the use of ChatGPT in education, finding it beneficial for various purposes, including generating academic content, brainstorming ideas, and rewriting text. While some participants raised concerns about potential biases and the need for informed use, the overall perception was positive. Additionally, the study proposed integrating ChatGPT into 2 specific courses in the master's programs in medicine and medical informatics. The incorporation of ChatGPT was envisioned to enhance student learning experiences and assist in project planning, programming code generation, examination preparation, workflow exploration, and technical interview preparation, thus advancing medical informatics education. In medical teaching, it will be used as an assistant for simplifying the explanation of concepts and solving complex problems, as well as for generating clinical narratives and patient simulators. CONCLUSIONS: The study's valuable insights into medical faculty students' perspectives and integration proposals for ChatGPT serve as an informative guide for professors aiming to enhance medical informatics education. The research delves into the potential of ChatGPT, emphasizes the necessity of collaboration in academic environments, identifies subject areas with discernible benefits, and underscores its transformative role in fostering innovative and engaging learning experiences. The envisaged proposals hold promise in empowering future health care professionals to work in the rapidly evolving era of digital health care.
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Informática Médica , Estudiantes de Medicina , Humanos , Inteligencia Artificial , Escolaridad , Docentes MédicosRESUMEN
Online digital data from media platforms have the potential to complement biodiversity monitoring efforts. We propose a strategy for integrating these data into current biodiversity datasets in light of the Kunming-Montreal Global Biodiversity Framework.
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Biodiversidad , Conservación de los Recursos NaturalesAsunto(s)
Aneurisma de la Aorta Abdominal , Rotura de la Aorta , Puntaje de Propensión , Humanos , Aneurisma de la Aorta Abdominal/cirugía , Rotura de la Aorta/cirugía , Masculino , Femenino , Anciano , Resultado del Tratamiento , Procedimientos Endovasculares/efectos adversos , Procedimientos Endovasculares/educación , Competencia Clínica , Implantación de Prótesis Vascular/efectos adversos , Implantación de Prótesis Vascular/educación , Estudios Retrospectivos , Anciano de 80 o más Años , Factores de RiesgoRESUMEN
Sub-cellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with an unprecedented level of precision. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the sub-cellular environment, is labor-intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework to interpret the diffusional 2D or 3D temporal behavior of objects in a rapid and efficient manner, agnostically. Demonstrating its versatility, we have applied DeepSPT to automated mapping of the early events of viral infections, identifying distinct types of endosomal organelles, and clathrin-coated pits and vesicles with up to 95% accuracy and within seconds instead of weeks. The fact that DeepSPT effectively extracts biological information from diffusion alone illustrates that besides structure, motion encodes function at the molecular and subcellular level.
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The aim of this European interprofessional Health Informatics (HI) Summer School was (i) to make advanced healthcare students familiar with what HI can offer in terms of knowledge development for patient care and (ii) to give them an idea about the underlying technical and legal mechanisms. According to the students' evaluation, interprofessional education was very well received, problem-based learning focussing on cases was rated positively and the learning goals were met. However, it was criticised that the online material provided was rather detailed and comprehensive and could have been a bit overcharging for beginners. These drawbacks were obviously compensated by the positive experience of working in international and interprofessional groups and a generally welcoming environment.
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Informática Médica , Instituciones Académicas , Humanos , Instituciones de Salud , Conocimiento , Aprendizaje Basado en ProblemasRESUMEN
Objective: Portuguese nationwide estimates indicate that 20% of abdominal aortic aneurysms (AAAs) are treated when ruptured. In these cases, intra-operative unfractionated heparin (UFH) usage rates vary widely. Evidence on this topic is scarce and focused on patients treated by open repair (OSR). The aim was to determine the influence of UFH on peri-operative thromboembolic events (TEs) and death in a cohort of ruptured AAA (rAAA). Methods: Retrospective, single-centre, comparative study. From 2011 to April 2023, all consecutive rAAAs (endovascular repair [EVAR] and OSR) were considered. Primary outcomes were 30-day TE free survival and TE rates. The secondary outcome was 30-day death. Safety endpoints were procedural blood loss, blood product requirements, and secondary interventions due to haemorrhage. Using propensity score matching (PSM) each UFH patient was matched with one no UFH patient in a 1:1 ratio. Results: The study included 250 patients. After PSM, 190 patients were analysed (EVAR: 60.0% no-UFH vs. 64.4% UFH). TE free survival estimates favoured the UFH group (67.3% vs. 47.2%, p = .009; UFH adjusted odds ratio [aOR] 2.01, 95% confidence interval [CI] 1.04-4.17). TEs were more frequent in the no UFH group (20.0% vs. 44.2% patients, p < .001; UFH aOR 0.31, 95% CI 0.15-0.65 for any TE), driven by an increase in bowel ischaemia (17.9% no UFH vs. 3.2% UFH, p = .001). Most events occurred in the first 72 hours. EVAR was associated with reduced TE and improved TE free survival (aOR 0.20, 95% CI 0.09-0.45 and aOR 5.54, 95% CI 2.34-13.08, respectively). No significant differences in 30-day survival were noted (75% no-UFH vs. 83% UFH, p = .26; aOR 1.08, 95% CI 0.48-2.43) nor in blood loss, peri-operative red blood cell and fresh frozen plasma requirements, or secondary interventions due to haemorrhage (p = .10; p = .11; p = .13 and p = .18 respectively). Conclusion: In this cohort, intra-operative UFH was safe and associated with improved TE free survival, driven by a reduction in bowel ischaemia. Conversely, mortality remained unaffected. Randomised controlled trials are required to confirm these findings.
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Citizen science plays a crucial role in helping monitor biodiversity and inform conservation. With the widespread use of smartphones, many people share biodiversity information on social media, but this information is still not widely used in conservation. Focusing on Bangladesh, a tropical megadiverse and mega-populated country, we examined the importance of social media records in conservation decision-making. We collated species distribution records for birds and butterflies from Facebook and Global Biodiversity Information Facility (GBIF), grouped them into GBIF-only and combined GBIF and Facebook data, and investigated the differences in identifying critical conservation areas. Adding Facebook data to GBIF data improved the accuracy of systematic conservation planning assessments by identifying additional important conservation areas in the northwest, southeast, and central parts of Bangladesh, extending priority conservation areas by 4,000-10,000 km2 . Community efforts are needed to drive the implementation of the ambitious Kunming-Montreal Global Biodiversity Framework targets, especially in megadiverse tropical countries with a lack of reliable and up-to-date species distribution data. We highlight that conservation planning can be enhanced by including available data gathered from social media platforms.
Registros de las redes sociales para guiar la planeación de la conservación Resumen La ciencia ciudadana es importante para monitorear la biodiversidad e informar la conservación. Con el creciente uso de los teléfonos inteligentes, muchas personas comparten información de la biodiversidad en redes sociales, pero todavía no se usa ampliamente en la conservación. Analizamos la importancia de los registros de las redes sociales para las decisiones de conservación enfocados en Bangladesh, un país tropical megadiverso y mega poblado. Cotejamos los registros de distribución de especies de aves y mariposas en Facebook y Global Biodiversity Information Facility (GBIF), las agrupamos en datos sólo de GBIF o datos combinados de Facebook y GBIF e investigamos las diferencias en la identificación de las áreas de conservación críticas. La combinación de los datos de Facebook con los de GBIF mejoró la precisión de las evaluaciones de la planeación de la conservación sistemática al identificar otras áreas importantes de conservación en el noroeste, sureste y centro de Bangladesh, extendiendo así las áreas prioritarias de conservación en unos 4,000-10,000 km2 . Se requieren esfuerzos comunitarios para impulsar la implementación de los objetivos ambiciosos del Marco Global de Biodiversidad Kunming-Montreal, especialmente en países tropicales que carecen de datos confiables y actuales sobre la distribución de las especies. Destacamos que la planeación de la conservación puede mejorarse si se incluye información tomada de las redes sociales.
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Mariposas Diurnas , Medios de Comunicación Sociales , Humanos , Animales , Conservación de los Recursos Naturales , Biodiversidad , AvesRESUMEN
Sub-cellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with an unprecedented level of precision. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the sub-cellular environment, is labor-intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework to interpret the diffusional 2D or 3D temporal behavior of objects in a rapid and efficient manner, agnostically. Demonstrating its versatility, we have applied DeepSPT to automated mapping of the early events of viral infections, identifying distinct types of endosomal organelles, and clathrin-coated pits and vesicles with up to 95% accuracy and within seconds instead of weeks. The fact that DeepSPT effectively extracts biological information from diffusion alone indicates that besides structure, motion encodes function at the molecular and subcellular level.
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Mammalian Notch signaling occurs when binding of Delta or Jagged to Notch stimulates proteolytic release of the Notch intracellular domain (NICD), which enters the nucleus to regulate target gene expression. To determine the temporal dynamics of events associated with Notch signaling under native conditions, we fluorescently tagged Notch and Delta at their endogenous genomic loci and visualized them upon pairing of receiver (Notch) and sender (Delta) cells as a function of time after cell contact. At contact sites, Notch and Delta immediately accumulated at 1:1 stoichiometry in synapses, which resolved by 15-20 min after contact. Synapse formation preceded entrance of the Notch extracellular domain into the sender cell and accumulation of NICD in the nucleus of the receiver cell, which approached a maximum after â¼45 min and was prevented by chemical and genetic inhibitors of signaling. These findings directly link Notch-Delta synapse dynamics to NICD production with unprecedented spatiotemporal precision.
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Social media platforms are a valuable source of data for investigating cultural and political trends related to public interest in nature and conservation. Here, we use the micro-blogging social network Twitter to explore trends in public interest in Brazilian protected areas (PAs). We identified ~400,000 Portuguese language tweets pertaining to all categories of Brazilian PAs over a ten-year period (1 January 2011-31 December 2020). We analysed the content of these tweets and calculated metrics of user engagement (likes and retweets) to uncover patterns and drivers of public interest in Brazilian PAs. Our results indicate that users / tweets mentioning PAs remained stable throughout the sample period. However, engagement with tweets grew steeply, particularly from 2018 onward and coinciding with a change in the Brazilian federal government. Furthermore, public interest was not evenly distributed across PAs; while national parks were the subject of the most tweets, mainly related to tourism activities, tweets related to conflicts among park users and managers were more likely to engage Twitter users. Our study highlights that automatic or semi-automatic monitoring of social media content and engagement has great potential as an early warning system to identify emerging conflicts and to generate data and metrics to support PA policy, governance and management.
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Medios de Comunicación Sociales , Humanos , Brasil , Blogging , LenguajeRESUMEN
Knowledge of biodiversity is unevenly distributed across the Tree of Life. In the long run, such disparity in awareness unbalances our understanding of life on Earth, influencing policy decisions and the allocation of research and conservation funding. We investigated how humans accumulate knowledge of biodiversity by searching for consistent relationships between scientific (number of publications) and societal (number of views in Wikipedia) interest, and species-level morphological, ecological, and sociocultural factors. Across a random selection of 3019 species spanning 29 Phyla/Divisions, we show that sociocultural factors are the most important correlates of scientific and societal interest in biodiversity, including the fact that a species is useful or harmful to humans, has a common name, and is listed in the International Union for Conservation of Nature Red List. Furthermore, large-bodied, broadly distributed, and taxonomically unique species receive more scientific and societal attention, whereas colorfulness and phylogenetic proximity to humans correlate exclusively with societal attention. These results highlight a favoritism toward limited branches of the Tree of Life, and that scientific and societal priorities in biodiversity research broadly align. This suggests that we may be missing out on key species in our research and conservation agenda simply because they are not on our cultural radar.