Procedimiento para la eliminación de documentos administrativos institucionales / Procedure for the elimination of institutional administrative documents

Los presentes Lineamientos tiene como objetivo, estandarizar las actividades a cumplir por el personal responsable de la eliminación de documentos administrativos para volver eficiente el procedimiento y reducir costos de conservación. Están sujetos al cumplimiento del presente procedimiento, todas las dependencias del Minsal y Establecimientos de Salud que generan archivos institucionales de valor primario (administrativo), iniciando con la identificación de la documentación que ya cumplió su plazo de conservación establecido en la TPCD, concluyendo con la destrucción de la documentación

The objective of these Guidelines is to standardize the activities to be carried out by the personnel responsible for the elimination of administrative documents to make the procedure efficient and reduce conservation costs. All Minsal agencies and Health Establishments that generate institutional files of primary (administrative) value are subject to compliance with this procedure, starting with the identification of documentation that has already met its conservation period established in the TPCD, concluding with the destruction of documentation

How do bicyclists respond to vehicles with adaptive headlamp systems? A nighttime study in an immersive virtual environment.

INTRODUCTION: The risk of motor vehicle-bicyclist crashes and fatalities is greater during nighttime than daytime lighting conditions, even though there are fewer cyclists on roadways at night. Vehicle Adaptive Headlamp Systems (AHS) aim to increase the visibility of bicyclists for drivers by directing a spotlight to illuminate bicyclists on or near the roadway. AHS technology also serves to alert bicyclists to the approaching vehicle by illuminating the road beneath the rider and by projecting a warning icon on the roadway. METHOD: Here, we examined how bicyclists respond to different AHS designs using a large screen, immersive virtual environment. Participants bicycled along a virtual road during nighttime lighting conditions and were overtaken by vehicles with and without an AHS system. The experiment included five treatment conditions with five different AHS designs. In each design a box of white light was projected beneath the rider; in four of the designs an icon was also projected on the road that varied in color (white or red) and position (to the left of the rider at midline or to the left of the front wheel). Participants in the control condition experienced only non-AHS vehicles. RESULTS: We found that riders in all AHS treatment conditions moved significantly farther away from overtaking vehicles with AHS systems, whereas riders in the control condition did not significantly move away from overtaking vehicles without AHS systems. PRACTICAL APPLICATIONS: The experiment demonstrates that AHS has potential to increase bicycling safety by influencing riders to steer away from overtaking vehicles.

Spatial-temporal pattern of colorectal cancer mortality in a Northeastern Brazilian State.

Colorectal cancer (CRC) is one of the most common cancer types worldwide. Its increasing mortality trends, especially in emerging countries, are a concern. The aim of this study was to analyse mortality trends and spatial patterns of CRC in the state of Sergipe, Brazil, from 1990 to 2019. Trends were calculated using data from the Online Mortality Atlas and Joinpoint Regression Program 4.8.0.1. Spatial analyses were performed using the empirical Bayesian model and Moran indices calculated by TerraView 4.2.2 between 1990 to 1999, 2000 to 2009 and 2010 to 2019. A total of 1585 deaths were recorded during the study period, with 58.42% among females. Trends were increasing and constant for both sexes and all age groups studied. The highest mean annual percent change was 6.2 {95% Confidence interval (CI) 3.4;9.0} for males aged +65 years and 4.5 (95% CI 3.2;5.8) for females aged 50-64 years. There was positive spatial autocorrelation for both sexes in all periods studied when using the Moran index for Bayesian rates. In summary, a consistent trend of increasing colorectal cancer (CRC) mortality has been observed overall. Nevertheless, an altered spatial distribution among males has emerged over the studied period.

Structure-changeable luminescent Eu(III) complex as a human cancer grade probing system for brain tumor diagnosis.

Accurate determination of human tumor malignancy is important for choosing efficient and safe therapies. Bioimaging technologies based on luminescent molecules are widely used to localize and distinguish active tumor cells. Here, we report a human cancer grade probing system (GPS) using a water-soluble and structure-changeable Eu(III) complex for the continuous detection of early human brain tumors of different malignancy grades. Time-dependent emission spectra of the Eu(III) complexes in various types of tumor cells were recorded. The radiative rate constants (kr), which depend on the geometry of the Eu(III) complex, were calculated from the emission spectra. The tendency of the kr values to vary depended on the tumor cells at different malignancy grades. Between T = 0 and T = 3 h of invasion, the kr values exhibited an increase of 4% in NHA/TS (benign grade II gliomas), 7% in NHA/TSR (malignant grade III gliomas), and 27% in NHA/TSRA (malignant grade IV gliomas). Tumor cells with high-grade malignancy exhibited a rapid upward trend in kr values. The cancer GPS employs Eu(III) emissions to provide a new diagnostic method for determining human brain tumor malignancy.

New records of Coronatella (Crustacea, Branchiopoda, Chydoridae) from Colombia with the first report of Coronatella undata and of the male of Coronatella monacantha / Novos registros de Coronatella (Crustacea, Branchiopoda, Chydoridae) na Colômbia com o primeiro relato de Coronatella undata e o macho de Coronatella monacantha

Biological samples obtained from a small temporary pond of northern Colombia yielded the first record Coronatella undata Sousa, Elmoor-Loureiro and Santos, 2015 and of the male of C. monacantha (Sars, 1901) for Colombia. In this study, the morphology of female of Coronatella undata and female and male of C. monacantha was described and compared to other species within the genus. C. undata was originally described from Brazil and, among the species of the Coronatella monacantha complex, seems to be closely related to C. acuticostata (Sars, 1903). C. undata shows some similarities with C. monacantha, but it can be identified by important diagnostic characters such as: 1) posterior-ventral corner of valve with two denticles, 2) seta on exopodite of trunk limb II rudimentary, 3) filter comb of trunk limb II with six setae, 4) ODL seta of trunk limb I shorter than longest seta of IDL. C. monacantha is the most reported species in the Neotropical region and the male most resemble C. paulinae Sousa, Elmoor-Loureiro & Santos, 2015 in relation to (i), length/wide of postabdomen ratio (ii) basal spine almost straight and (iii)) long basal spine reaching the mid-length of basal spine. However, they can be separated by (i) number of lateral seta on the antennule, (ii) postanal angle, (iii) position of gonopore (iv) presence of a denticle on posterior-ventral corner of valve.

Amostras biológicas obtidas de uma pequena lagoa temporária do norte da Colômbia proporcionaram o primeiro registro de Coronatella undata Sousa, Elmoor-Loureiro e Santos, 2015 e do macho de Coronatella monacantha (Sars, 1901) na Colômbia. Neste estudo, foi descrita a morfologia de fêmeas de C. undata e de fêmeas e machos de C. monacantha, comparando-a com outras espécies do gênero. Coronatella undata foi descrita originalmente no Brasil e, entre as espécies do complexo C. monacantha, parece estar intimamente relacionada com Coronatella acuticostata (Sars, 1903). Coronatella undata apresenta algumas semelhanças com C. monacantha, mas pode ser identificada por seus principais caracteres, tais como: 1) ângulo posterior ventral da valva com dois dentículos; 2) cerda rudimentar no exopodito do ramo do tronco II; 3) filtro da gnatobase do apêndice torácico II com seis cerdas; 4) cerda ODL do membro do tronco I mais curta que a cerda mais longa do IDL. Coronatella monacantha é a espécie mais relatada na região neotropical, e o macho se assemelha mais a Coronatella paulinae Sousa, Elmoor-Loureiro & Santos em relação à/ao: (i) razão comprimento / largura do pós-abdômen, (ii) espinho basal quase reto e (iii) espinho basal longo com a metade do comprimento do espinho basal. No entanto, eles podem ser separados pelo/pela: (i) número de cerdas laterais na antênula, (ii) ângulo postanal, (iii) posição do gonóporo e (iv) presença de dentículo no canto ventral posterior da valva.

Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification.

Whole Slide Images (WSIs) or histopathology images are used in digital pathology. WSIs pose great challenges to deep learning models for clinical diagnosis, owing to their size and lack of pixel-level annotations. With the recent advancements in computational pathology, newer multiple-instance learning-based models have been proposed. Multiple-instance learning for WSIs necessitates creating patches and uses the encoding of these patches for diagnosis. These models use generic pre-trained models (ResNet-50 pre-trained on ImageNet) for patch encoding. The recently proposed KimiaNet, a DenseNet121 model pre-trained on TCGA slides, is a domain-specific pre-trained model. This paper shows the effect of domain-specific pre-training on WSI classification. To investigate the effect of domain-specific pre-training, we considered the current state-of-the-art multiple-instance learning models, 1) CLAM, an attention-based model, and 2) TransMIL, a self-attention-based model, and evaluated the models' confidence and predictive performance in detecting primary brain tumors - gliomas. Domain-specific pre-training improves the confidence of the models and also achieves a new state-of-the-art performance of WSI-based glioma subtype classification, showing a high clinical applicability in assisting glioma diagnosis. We will publicly share our code and experimental results at https://github.com/soham-chitnis10/WSI-domain-specific.

Finding the best trade-off between performance and interpretability in predicting hospital length of stay using structured and unstructured data.

OBJECTIVE: This study aims to develop high-performing Machine Learning and Deep Learning models in predicting hospital length of stay (LOS) while enhancing interpretability. We compare performance and interpretability of models trained only on structured tabular data with models trained only on unstructured clinical text data, and on mixed data. METHODS: The structured data was used to train fourteen classical Machine Learning models including advanced ensemble trees, neural networks and k-nearest neighbors. The unstructured data was used to fine-tune a pre-trained Bio Clinical BERT Transformer Deep Learning model. The structured and unstructured data were then merged into a tabular dataset after vectorization of the clinical text and a dimensional reduction through Latent Dirichlet Allocation. The study used the free and publicly available Medical Information Mart for Intensive Care (MIMIC) III database, on the open AutoML Library AutoGluon. Performance is evaluated with respect to two types of random classifiers, used as baselines. RESULTS: The best model from structured data demonstrates high performance (ROC AUC = 0.944, PRC AUC = 0.655) with limited interpretability, where the most important predictors of prolonged LOS are the level of blood urea nitrogen and of platelets. The Transformer model displays a good but lower performance (ROC AUC = 0.842, PRC AUC = 0.375) with a richer array of interpretability by providing more specific in-hospital factors including procedures, conditions, and medical history. The best model trained on mixed data satisfies both a high level of performance (ROC AUC = 0.963, PRC AUC = 0.746) and a much larger scope in interpretability including pathologies of the intestine, the colon, and the blood; infectious diseases, respiratory problems, procedures involving sedation and intubation, and vascular surgery. CONCLUSIONS: Our results outperform most of the state-of-the-art models in LOS prediction both in terms of performance and of interpretability. Data fusion between structured and unstructured text data may significantly improve performance and interpretability.

Graph-based multi-modality integration for prediction of cancer subtype and severity.

Personalised cancer screening before therapy paves the way toward improving diagnostic accuracy and treatment outcomes. Most approaches are limited to a single data type and do not consider interactions between features, leaving aside the complementary insights that multimodality and systems biology can provide. In this project, we demonstrate the use of graph theory for data integration via individual networks where nodes and edges are individual-specific. We showcase the consequences of early, intermediate, and late graph-based fusion of RNA-Seq data and histopathology whole-slide images for predicting cancer subtypes and severity. The methodology developed is as follows: (1) we create individual networks; (2) we compute the similarity between individuals from these graphs; (3) we train our model on the similarity matrices; (4) we evaluate the performance using the macro F1 score. Pros and cons of elements of the pipeline are evaluated on publicly available real-life datasets. We find that graph-based methods can increase performance over methods that do not study interactions. Additionally, merging multiple data sources often improves classification compared to models based on single data, especially through intermediate fusion. The proposed workflow can easily be adapted to other disease contexts to accelerate and enhance personalized healthcare.

Explainable Convolutional Neural Networks for Brain Cancer Detection and Localisation.

Brain cancer is widely recognised as one of the most aggressive types of tumors. In fact, approximately 70% of patients diagnosed with this malignant cancer do not survive. In this paper, we propose a method aimed to detect and localise brain cancer, starting from the analysis of magnetic resonance images. The proposed method exploits deep learning, in particular convolutional neural networks and class activation mapping, in order to provide explainability by highlighting the areas of the medical image related to brain cancer (from the model point of view). We evaluate the proposed method with 3000 magnetic resonances using a free available dataset. The results we obtained are encouraging. We reach an accuracy ranging from 97.83% to 99.67% in brain cancer detection by exploiting four different models: VGG16, ResNet50, Alex_Net, and MobileNet, thus showing the effectiveness of the proposed method.

Improved YOLOv7 Algorithm for Detecting Bone Marrow Cells.

The detection and classification of bone marrow (BM) cells is a critical cornerstone for hematology diagnosis. However, the low accuracy caused by few BM-cell data samples, subtle difference between classes, and small target size, pathologists still need to perform thousands of manual identifications daily. To address the above issues, we propose an improved BM-cell-detection algorithm in this paper, called YOLOv7-CTA. Firstly, to enhance the model's sensitivity to fine-grained features, we design a new module called CoTLAN in the backbone network to enable the model to perform long-term modeling between target feature information. Then, in order to cooperate with the CoTLAN module to pay more attention to the features in the area to be detected, we integrate the coordinate attention (CoordAtt) module between the CoTLAN modules to improve the model's attention to small target features. Finally, we cluster the target boxes of the BM cell dataset based on K-means++ to generate more suitable anchor boxes, which accelerates the convergence of the improved model. In addition, in order to solve the imbalance between positive and negative samples in BM-cell pictures, we use the Focal loss function to replace the multi-class cross entropy. Experimental results demonstrate that the best mean average precision (mAP) of the proposed model reaches 88.6%, which is an improvement of 12.9%, 8.3%, and 6.7% compared with that of the Faster R-CNN model, YOLOv5l model, and YOLOv7 model, respectively. This verifies the effectiveness and superiority of the YOLOv7-CTA model in BM-cell-detection tasks.

The Importance of Quality Assurance in Radiation Oncology Clinical Trials.

Clinical trials have been the center of progress in modern medicine. In oncology, we are fortunate to have a structure in place through the National Clinical Trials Network (NCTN). The NCTN provides the infrastructure and a forum for scientific discussion to develop clinical concepts for trial design. The NCTN also provides a network group structure to administer trials for successful trial management and outcome analyses. There are many important aspects to trial design and conduct. Modern trials need to ensure appropriate trial conduct and secure data management processes. Of equal importance is the quality assurance of a clinical trial. If progress is to be made in oncology clinical medicine, investigators and patient care providers of service need to feel secure that trial data is complete, accurate, and well-controlled in order to be confident in trial analysis and move trial outcome results into daily practice. As our technology has matured, so has our need to apply technology in a uniform manner for appropriate interpretation of trial outcomes. In this article, we review the importance of quality assurance in clinical trials involving radiation therapy. We will include important aspects of institution and investigator credentialing for participation as well as ongoing processes to ensure that each trial is being managed in a compliant manner. We will provide examples of the importance of complete datasets to ensure study interpretation. We will describe how successful strategies for quality assurance in the past will support new initiatives moving forward.

iHerd: an integrative hierarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease.

Different genes form complex networks within cells to carry out critical cellular functions, while network alterations in this process can potentially introduce downstream transcriptome perturbations and phenotypic variations. Therefore, developing efficient and interpretable methods to quantify network changes and pinpoint driver genes across conditions is crucial. We propose a hierarchical graph representation learning method, called iHerd. Given a set of networks, iHerd first hierarchically generates a series of coarsened sub-graphs in a data-driven manner, representing network modules at different resolutions (e.g., the level of signaling pathways). Then, it sequentially learns low-dimensional node representations at all hierarchical levels via efficient graph embedding. Lastly, iHerd projects separate gene embeddings onto the same latent space in its graph alignment module to calculate a rewiring index for driver gene prioritization. To demonstrate its effectiveness, we applied iHerd on a tumor-to-normal GRN rewiring analysis and cell-type-specific GCN analysis using single-cell multiome data of the brain. We showed that iHerd can effectively pinpoint novel and well-known risk genes in different diseases. Distinct from existing models, iHerd's graph coarsening for hierarchical learning allows us to successfully classify network driver genes into early and late divergent genes (EDGs and LDGs), emphasizing genes with extensive network changes across and within signaling pathway levels. This unique approach for driver gene classification can provide us with deeper molecular insights. The code is freely available at https://github.com/aicb-ZhangLabs/iHerd. All other relevant data are within the manuscript and supporting information files.

Understanding Human-Computer Interactions in Restricted Clinical Environments.

INTRODUCTION: Conducting research on human-computer interaction and information retrieval requires unobtrusive observations within existing network architectures. STATE OF THE ART: Most of the available tools are not suitable to be applied within restricted clinical systems. The specific requirements hinder analysis of the human factors in health sciences. CONCEPT: We identified extensions for popular web browsers as a suitable way to conduct studies in highly regulated environments. IMPLEMENTATION: Considering the specialized requirements and an adequate level of transparency for the recorded clinician, we developed an open-source Web Extension compatible with major web browsers. LESSONS LEARNED: We identified the challenges associated with the specific tool and are preparing its use to understand clinical reasoning in personalized oncology.

Outcomes and Complications Associated With Caudal Thoracic and Abdominal Air Sac Cannulation in 68 Birds.

Air sac cannulation is used both as an emergency procedure in avian patients with severe upper respiratory compromise, as well as a means of routine ventilation for surgery of the head and neck. The objective of this retrospective study was to describe and quantify the complications associated with air sac cannulation in birds. Medical records were retrieved for all patients that underwent caudal thoracic or abdominal air sac cannulation at a single center between August 2004 and October 2020. Patient signalment, indication for air sac cannulation, location of air sac cannula (ASC) placement, occurrence and category of complications encountered, and survival data were recorded. Eighty-four ASCs were placed in 68 birds across 6 orders; 95.2% (80/84) of cases survived general anesthesia for initial ASC placement. The side and position of ASC placement were known in 33.3% (28/84) and 21.4% (18/84) of cases, respectively. Survival to ASC removal was known in 91.3% (73/80) of cases; 43 (58.9%) of these 73 cases survived to ASC removal. Complications were observed in 32.5% (26/80) of cases, and 11.5% (3/26) of cases died as a direct result of the complication. The most common reported ASC complication was loss of patency in 23.8% (19/80) of cases. Increased likelihoods for complications were seen in cases where exercise intolerance (P = 0.04) or abnormal respiratory sounds (P = 0.04) were reported at presentation. Increased likelihoods for survival to ASC removal were seen with intercostal placements (P = 0.049) and peri-interventional antibiotic therapy (P = 0.005). Decreased likelihood for survival to ASC removal was seen in cases where voice change was reported at presentation (P = 0.02). This study demonstrates a moderate risk of ASC complication, with a guarded overall prognosis for survival to ASC removal.

Taxonomy and effectiveness of clinician agenda-setting questions in routine ambulatory encounters: A mixed method study.

OBJECTIVES: Despite decades of communication training, studies repeatedly demonstrate that clinicians fail to elicit patients' agendas. Our goal was to provide clinicians with actionable guidance about the effectiveness of agenda-soliciting questions. METHODS: We coded clinician agenda-soliciting questions and patient responses in audio-recorded ambulatory encounters at an urban academic hospital. To evaluate the association between question type and odds of the patient raising a concern, we performed mixed-effects logistic regression. RESULTS: We identified 346 agenda-soliciting questions within 138 visits (mean 2.51/visit; range 0-9). Agenda-soliciting questions were categorized as personal state inquiries (37%, "How are you?"), feeling-focused (5% "How're you feeling?"), problem-focused (12%; "Are you having any problems"), direct solicitations (3%; "Anything you want to discuss today?"), "what else" (3%), "anything else" (14%), leading (16%; "Nothing else today?"), and space-reducing (11% "Anything else? Smoking?"). Patients raised a concern in response to 107 clinician questions (27%). Patients were more likely to raise a concern to direct solicitation (OR 22.95, 95% CI 2.62-200.70) or "what else" (OR 4.68, 95% CI 1.05-20.77) questions. CONCLUSIONS: The most effective agenda-soliciting questions are used least frequently by clinicians. PRACTICE IMPLICATIONS: Clinicians should elicit patient agendas by using direct language, and solicit additional concerns using "what else" vs. "anything else" questions.

Mapping the characteristics, concepts and methodologies of matching-adjusted indirect comparison studies assessing pharmacological therapies in oncology: a scoping review protocol.

INTRODUCTION: Matching-adjusted indirect comparison (MAIC) studies are a subtype of indirect comparison, which uses propensity score weighting to enhance comparability. This method adjusts aggregated data based on covariables from individual patient data from studies to produce population-adjusted indirect comparisons. Some national Health Technology Assessment agencies have recently received submissions containing MAIC models. However, there can be a lack of confidence in its estimates when they are poorly reported and inconsistent with other techniques. The objective of this study is to map the characteristics, concepts and methodology of MAIC studies used for pharmacological therapies in the field of oncology. METHODS AND ANALYSIS: A scoping review methodology will be applied following the Joanna Briggs Institute framework and the results will be reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews. Studies that used MAIC to compare treatments in oncology conditions will be considered eligible. A systematic search will be conducted in PubMed, Embase and the Cochrane Library. No restriction of location or language will be applied. Study screening will be documented and presented in a Preferred Reporting Items for Systematic reviews and Meta-Analyses flow diagram. Data will be extracted and recorded on a predefined data form and will be presented in a tabular form accompanied by a descriptive summary. ETHICS AND DISSEMINATION: No ethical approval is required for this study. The results of this scoping review will be disseminated through peer-reviewed publications.

Identification of subject-specific responses to footwear during running.

Placing a stronger focus on subject-specific responses to footwear may lead to a better functional understanding of footwear's effect on running and its influence on comfort perception, performance, and pathogenesis of injuries. We investigated subject-specific responses to different footwear conditions within ground reaction force (GRF) data during running using a machine learning-based approach. We conducted our investigation in three steps, guided by the following hypotheses: (I) For each subject x footwear combination, unique GRF patterns can be identified. (II) For each subject, unique GRF characteristics can be identified across footwear conditions. (III) For each footwear condition, unique GRF characteristics can be identified across subjects. Thirty male subjects ran ten times at their preferred (self-selected) speed on a level and approximately 15 m long runway in four footwear conditions (barefoot and three standardised running shoes). We recorded three-dimensional GRFs for one right-foot stance phase per running trial and classified the GRFs using support vector machines. The highest median prediction accuracy of 96.2% was found for the subject x footwear classification (hypothesis I). Across footwear conditions, subjects could be discriminated with a median prediction accuracy of 80.0%. Across subjects, footwear conditions could be discriminated with a median prediction accuracy of 87.8%. Our results suggest that, during running, responses to footwear are unique to each subject and footwear design. As a result, considering subject-specific responses can contribute to a more differentiated functional understanding of footwear effects. Incorporating holistic analyses of biomechanical data is auspicious for the evaluation of (subject-specific) footwear effects, as unique interactions between subjects and footwear manifest in versatile ways. The applied machine learning methods have demonstrated their great potential to fathom subject-specific responses when evaluating and recommending footwear.

Multi-motor position synchronization control method based on non-singular fast terminal sliding mode control.

In order to improve the position high-precision synchronization performance of multi-motor synchronous control, a multi-motor position synchronization control method based on non-singular fast terminal sliding mode control (NFTSMC) combined with an improved deviation coupling control structure (Improved Deviation Coupling Control(IDCC), NFTSMC+IDCC). Firstly, this paper designs a sliding mode controller using a non-singular fast terminal sliding mode surface with a Permanent Magnet Synchronous Motor (PMSM) as the control object. Secondly, the deviation coupling is improved to enhance the coupling between multiple motors and achieve position synchronization. Finally, the simulation results show that the total error of multi-motor position synchronization under NFTSMC control is 0.553r in the simulation of multi-motor synchronization control under the same working conditions, which is 2.873r and 1.772r less than that of SMC and FTSMC in terms of speed error, and the anti-disturbance performance is 83.68% and 76.22% higher than that of both of them, respectively. In the subsequent simulation of the improved multi-motor position synchronization structure, the total error of the multi-motor position is in the range of 0.56r-0.58r at three speeds, which is much smaller than the synchronization error under the Ring Coupling Control (RCC) structure and Deviation Coupling Control (DCC) structure, showing a better The synchronization error is much smaller than that of the RCC structure and DCC structure, which shows better position synchronization performance. Therefore, the multi-motor position synchronization control method proposed in this paper has a good position synchronization effect and achieves the control effect of small displacement error and fast convergence of the multi-motor position synchronization control system after being disturbed, the control performance is significantly improved.

What Kind of Ontologies Do We Need in the Biomedical Domain?

We tackle the question as to what sort of ontologies we primarily need in the biomedical domain. For this purpose, we will first provide a simple categorization of ontologies and describe an important use case related to modeling and documenting events. Then, the impact of using upper-level ontologies as a basis to address our use case will be shown in order to derive an answer to our research question. Although formal ontologies can serve as a starting point to understand conceptualization in a domain and facilitate interesting inferences, it is even more important to account for the dynamic and changing nature of knowledge. Being unconstrained by pre-defined categories and relationships can facilitate timely enrichment of a conceptual scheme and provide links and dependency structures in an informal manner. Semantic enrichment can be achieved by other mechanisms such as tagging or the creation of synsets as, for example, provided in WordNet.

The order of sequential exposure of U2OS cells to gamma and alpha radiation influences the formation and decay dynamics of NBS1 foci.

DNA double strand breaks (DSBs) are a deleterious form of DNA damage. Densely ionising alpha radiation predominantly induces complex DSBs and sparsely ionising gamma radiation-simple DSBs. We have shown that alphas and gammas, when applied simultaneously, interact in producing a higher DNA damage response (DDR) than predicted by additivity. The mechanisms of the interaction remain obscure. The present study aimed at testing whether the sequence of exposure to alphas and gammas has an impact on the DDR, visualised by live NBS1-GFP (green fluorescent protein) focus dynamics in U2OS cells. Focus formation, decay, intensity and mobility were analysed up to 5 h post exposure. Focus frequencies directly after sequential alpha → gamma and gamma → alpha exposure were similar to gamma alone, but gamma → alpha foci quickly declined below the expected values. Focus intensities and areas following alpha alone and alpha → gamma were larger than after gamma alone and gamma → alpha. Focus movement was most strongly attenuated by alpha → gamma. Overall, sequential alpha → gamma exposure induced the strongest change in characteristics and dynamics of NBS1-GFP foci. Possible explanation is that activation of the DDR is stronger when alpha-induced DNA damage precedes gamma-induced DNA damage.