dataesr/scientific-paragraphs-categorization huggingface dataset:
3D surface rendering and cell segmentation with Imaris. The image analysis software Imaris provides tools for the generation of "object surfaces" based on imaging data, which can be used to model cells and structures in tissues in 3D. Surfaces can not only be used for identification and visualization of individual cells but also act as a container from which all desired statistics can be calculated. For example, surfaces for cDC1 can be used to gather information such as spatial positioning of individual cells within the image (Fig. 13F ), distance to other cellular (e. g. , T cells) or structural objects (e. g. , blood vessels) or signal intensity statistics for additional fluorescence markers to evaluate protein expression levels. The following list provides tips on the most important steps for the generation of 3D surfaces for cDC1 in Imaris v.
Top tricks and pitfalls. • Accurate 3D surfaces depend on high-quality imaging data. Therefore, antibody concentration and staining should be optimized according to the specific properties of the tissue and the cells of interest. In addition, imaging with high resolution is necessary to provide sufficient details for correct surface generation (preferably a 40× objective or higher, spacing of Z stacks ≤1 μm). Oversampling during acquisition can further help improve image deconvolution. • Finding the ideal setup for cell segmentation often requires empirical testing of surface rendering parameters for each cell type and tissue. Furthermore, Imaris offers the possibility to manually correct oversegmentation of individual cells by object unification, which can be used for smaller datasets.
Note that these data can further be exported for additional analyses by other software platforms to help determine statistical significance (e. g. , Prism or Excel) or facilitate more complex population gating (see section 4. 4. 2). • Use the "Filter" tab for identifying cDC1 in close proximity to T cells, e. g. cells with a shortest distance up to a 2 μm threshold. Copy these objects into a new surface for visualization in Imaris and analysis of additional parameters. In addition to manual thresholding, color-graded visualization of spatial statistics can be achieved using the "Statistics-coded" option in the 'Color' tab. This tool can be useful to quickly assess patterns of interacting cells and identify cell clusters in larger datasets.
Histocytometry population analysis For phenotypic analyses of cell populations in multiparameter datasets, it is helpful to implement additional software into the workflow, e. g. FlowJo, which allows for more dynamic, hierarchical population gating. Relevant statistics for such histocytometry analyses include object position, sphericity, volume, channel intensity, and object-object distance. For such analyses, compile one Excel file in which each column contains one statistic and every row represents one cell object. Use the statistics names as column headers and export the file in CSV format. Opening these CSV files with FlowJo will generate an FCS file for further analysis. In Imaris, use object surface IDs to identify cells gated in FlowJo. An example for the identification of IL-12-producing cDC1 in tissues by such a workflow is shown in Fig.
Data analysis The multispectral imaging system enables the identification and downstream quantification of multiple overlapping biomarkers without the autofluorescence interference as signals are unmixed from one another. • Using QuPath, we design a 1 mm grid on the whole slide scan and randomly selected eight regions of interest per slide • We performed a tissue quality verification in the defined regions of interest, or re-generate eight regions • We made a code attribution to each identified area and manually count CLEC9A and IFNλ-1 double or single positive cells with HALO software • If events observed are rare, quantification can be done manually as described in Hubert et al. [60] , however algorithm exists for automatic counting using the HALO software. 6 Quantitative image analysis of host-pathogen interactions in an in vitro 3D human lung/DC model.
g. , a 96-well format. Thus, it is a very versatile tool allowing not only high content screening, but also more demanding applications, such as fixed-and live-cell assays, complex cell models or FRET. General remark: While this protocol specifically refers to Operetta CLS and Harmony software for imaging and data analysis, a similar workflow could be implemented and reproduced in other platforms for confocal images analysis (such as Amira, Imaris, ZEN, LAS X, FIJI, and ImageJ).
Combined Image and Data analysis using the Harmony software The Harmony TM high-content imaging and analysis software allows an easy set-up of assays, automation of experiments, image acquisition and data analysis. Additionally, the software contains a full range of incorporated ready-madesolutions (RMS) enabling analysis of most common assays, e. g. cell viability, apoptosis, cell cycle, cell proliferation, cytoskeletal rearrangement, receptor translocation, spot analysis, cell migration etc. that can be adapted to the individual applications. To distinguish between goblet and ciliated cells within differentiated normal human bronchial epithelial cell cultures, cells were stained using acetylated tubulin for ciliated cells (orange) and MUC5AC for goblet cells (green). In addition, nuclei were visualized with Hoechst.
Live/Dead cell analysis The 'RMS Live/Dead Cell Counting' is an easy way to quantify the percentage of cells in your assay that are responding to cytotoxic compounds, pathogens or secreted toxins from pathogens. The RMS identifies nuclei in the building block ´Find nuclei´of the Harmony software and within this region detects the amount of dead cell stain. Building blocks for this analysis are already integrated in the RMS Live/Dead Cell Counting. Further building blocks can be incorporated in the analysis depending on the user-specific experiment, e. g. to evaluate live or dead DC, a building block using a specific DC marker can be added in between the nuclei and dead cell stain building block. The ROI can be determined by adding the fluorochrome-coupled DC-specific antibodies (e. g. , CD11c for all DC).
XYZ-and 3D view of pathogen-DC interactions XYZ View: The XYZ-view displays the layers of a stack measurement in a special viewer of the Harmony software, where sectional planes in x, y-and z-direction are illustrated. The X-Y or top view of the stack is the large one in the middle, X-Z view at the top ( = front view), and Y-Z (right side view) at the left (Fig. 21A ). The XYZ-view is created by: 1. Loading a suitable stack measurement 2. Selecting multiple planes from a single well 3. Processing the stack using either MAXIMUM PROJECTION (2D analysis) or 3D ANALYSIS: the XYZ view is calculated, which may take several minutes dependent on the data processed 4. Opening the XYZ View tab, that allows dragging the viewpoint in one of the three sections of the view. The other sectional planes are updated accordingly 3D View: Here the selected planes of a stack are used for 3D calculation of an image (Fig.
Spot Analysis This analysis is a valuable tool for enabling detection and quantification of spots (viruses, peroxisomes, endosomes etc. ) in DC. If using a second appropriate marker, e. g. , cytoplasmic or endosomal, the localization of the virus can be assessed by this method. For this, standalone building blocks in the Harmony software are combined, that can be saved and recalled, if performing spot analysis more often. Thus, a user-specific RMS is generated this way. This analysis was already recently published by our group  [74, 85]. How to build a RMS spot analysis is shown below, using HIV-1-infected DC as example: 1. Create a blank analysis sequence in the image analysis tab 2. Find Nuclei (choose nuclear stain) 3. Find Cytoplasm (choose cytoplasmic or endoplasmic stain, dependent on the read-out) 4.
Migration Analysis (RMS, Harmony software) of DC Due to much more complex shape of 3D objects compared to 2D, more morphology properties are necessary. Therefore, the set of measures within the Harmony software is designed to also enable identification of object orientation to evaluate the direction of migration. DC migration after pathogen recognition at entry sites plays a major role to initiating adaptive immune responses within the proximate lymph node. DC migration following pathogen exposure towards a chemokine-coated open area (e. g. , CCL19 and/or CCL21) that separates the cell layer from background can be monitored using the Harmony CellMigration-1 RMS. Unstimulated and infected CFSE-labeled DC can be tracked over time and quantified within the open area (Fig. 22 ). The procedure for live cell imaging is as follows: 1.
Acknowledgements: This collection represents key protocols routinely used by the authors, which hopefully will be useful to others in the field. Notably, a number of other methods for microscopy-based analysis of DC are used by other colleagues, and we have tried to mention them wherever possible. We apologize to colleagues whose work we have not included. Section 4: This work was supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation  -project numbers 442405234; 424926990; 449174900 and  61704785 ), the  German Cancer Aid  ( DKH, 70113847 ) and the  Elite Network of Bavaria, Bavarian State Ministry of Science and the Arts  ( N-LW-2016-370 ) to J. P. B; a  DFG Emmy Noether Grant  ( Schr 1444/1-1 ) and an  ERC Starting Grant  ( ERC-2016-STG-715182 ) to B. U. S. Section 5: This work was supported by funding from  INSERM ,  INCA  ( PLBIO INCa ),  ANRS ,  ARC, Ligue contre le Cancer (Régionale Auvergne-Rhône-Alpes et Saône-et-Loire ,  Comité de la Savoie ); We would like to thank our financial supports: the  Région Auvergne-Rhône-Alpes  and the  ANR , the  SIRIC  project (LYRIC, grant no.
1. 5 Top tricks and pitfalls 5. Next to IMARIS, open-source software alternatives and inter- esting packages include: 1. MtrackJ (https://imagej. net/plugins/mtrackj), 2. cellmigRation (https://github. com/ocbe-uio/cellmigRat- ion/), 3. The Baxter Algorithms (https://github. com/klasma/Baxter Algorithms), 4. CellMigration (https://github. com/quantixed/CellMigrat- ion) 5. TrackMate https://imagej. net/plugins/trackmate 1. Careful preparation of the lymph node is crucial. Any acci- dental and even unconscious damage can have a negative impact on the experiment. Special attention should be paid to this when initially learning the technique. It is recommended to practice the technique on a reporter mouse for myeloid cells, especially neutrophils, for example, a LysM GFP mouse.
The membership functions of P veh and SOC bat are 7 and 5 respectively. Hence, the dimensional number of fuzzy states x is m = 35. Traditionally, fuzzy rules can be constructed using experienced data and/or engineering experience. The logic rules are usually formed like: IF P veh is " Positive High" (PH), AND SOC bat is "Very Low"(VL), THEN P fc is "Super High" (SH). The inference engine deduces then the fuzzy output based on each rule. The control action is calculated by a defuzzifier combining all fuzzy outputs. For instance, the calculation can be realized using the weighted average defuzzification method: a = ∑ n i=1 u i μ i ∑ n i=1 μ i (13) where μ i is the i th weight for the output fuzzy action sets, u i is the i th compoment of the typical value vector U, which is related to each fuzzy set of P fc , and n = 8 is the number of fuzzy action.
Simulations and results analysis A Python-based training and testing platform have been established for the proposed Fuzzy REINFORCE based EMS. Moreover, a Hardwarein-Loop (HIL) experimental platform has also been built with dSPACE MicroLabBox, and a microcontroller ESP32. The performance of each calculation unit is as shown in Table  7. Note that, the model of FCHEV is based on Toyota's Mirai whose database can be found in  [32]. In this section, the results of the proposed EMS are analyzed and discussed.
Purpose A recent study published in the JMIR Med Educ Journal explored the potential impact of the Generative Pre-Train (ChatGPT), a generative language model, on medical education, research, and practice. In the present study, an interview with ChatGPT was conducted to determine its capabilities and potential for use in anatomy education (AE) and anatomy research (AR). Methods The study involved 18 questions asked of ChatGPT after obtaining an online subscription to the 4th edition. The questions were randomly selected and evaluated based on accuracy, relevance, and comprehensiveness.
Introduction For several years, consumers have had access to chatbots and conversational agents thanks to the rapid advancement in technology, specifically in the field of Artificial Intelligence (AI). In 2018, Open AI developed a language model called Generative Pre-Train (GPT), which eventually led to the creation of ChatGPT-3. 5 in November 2022. This chatbot was unlike any other, boasting approximately 175 billion parameters and 570 GB of data, enabling it to discuss a wide range of topics. In just its first month of release, ChatGPT-3. 5 garnered a whopping 57 million users and by January 2023, had reached 100 million users, making it the fastest-growing consumer application of all time. In the Anatomical Sciences Education Journal, a letter to the editor was published that presented a conversation between the author and ChatGPT-3.
Methods After an online subscription to ChatGPT (4th version, edition of 24th of May), several questions regarding its potential role for AE and AR were asked of the chatbot. The questions flow was based on the reply to our first question about the several roles that the generative language models can play in AE. Questions topics were randomly selected after a consensus between the authors. Further questions were included to investigate the ChatGPT's ability to address any issues related to anatomical variants and clinical and applied anatomy. All replies have not been copyedited or altered. Three authors (TT, KN, and MP), and professors of anatomy evaluated the ChatGPT's replies in terms of accuracy, relevance to the question, and comprehensiveness.
Results-the interview with the ChatGPT Interviewers (IVS) What is the role of generative language models in medical students' AE? ChatGPT: Generative language models like GPT-4 have the potential to revolutionize ME in many ways, including AE. Here are several roles they can play: 1. Detailed explanations: Generative models can provide explanations on different parts of the human anatomy, their functions, and interconnections, making the subject more understandable and engaging. 2. Question-replying: Students can ask detailed or specific questions about anatomy, and these models can generate replies based on the vast amount of data they have been trained on. This allows students to learn at their own pace, revisit concepts, and explore areas of specific interest. 3. Interactive learning: With these models, is possible to create interactive learning experiences.
3 IVS Let's talk about your second point, "Students can ask detailed or specific questions about anatomy, and these models can generate responses". Which are the branches of the axillary artery (AA)? ChatGPT; The AA, a major blood vessel of the upper limb, has several branches that supply various structures in the shoulder and upper limb. After the subclavian artery exits the first rib, it becomes the AA. The AA is traditionally divided into three parts, with each part having specific branches: -1st part: Is located between the outer border of the first rib and the medial border of the pectoralis minor muscle (Pm ChatGPT; As an AI developed by OpenAI, I'm capable of translating English text into several languages with a fair degree of accuracy. That includes but isn't limited to, the following languages: Spanish; French; German; Italian; Portuguese; Dutch; Russian; Chinese; Japanese; Korean; Arabic.
It's important to note, however, that while I strive for the highest accuracy, no translation can be guaranteed to be 100% accurate, especially with complex subjects like medical or anatomical terminology. Certain nuances or specialized vocabulary might not translate perfectly between languages. It's always a good idea to have important translations verified by a human expert in both the source and target languages. IVS Which of the following is the correct anatomical term? "Sacrotuberous ligament" or "major ischiosacral ligament"? ChatGPT; The correct anatomical term is "sacrotuberous ligament", a ligament that runs from the sacrum (lower part of the spine) to the ischial tuberosity in the hip bone. The term "major ischiosacral ligament" is not a recognized term in standard anatomical nomenclature.
IVS IVS Could you pick a highly cited article from surgical and radiologic anatomy and provide a summary for a medical student? ChatGPT; I'm sorry for the misunderstanding, but as of my last update in September 2021, I don't have the ability to access or search the internet in real-time, including specific databases or journals. Therefore, I can't pull up or summarize specific articles, whether from Surgical and Radiologic Anatomy or other sources. However, I can demonstrate how to summarize a scientific article using a fictitious example. Here's an illustration of how a summary for a medical student might look: Title: "Anatomical Variations in the Sural Nerve and Its Implications in Surgery: a systematic review" Summary: This review paper aimed to consolidate current knowledge on the anatomical variations of the sural nerve and discuss its surgical implications.
Remember that when reading a scientific paper, it's important to understand the objective, methodology, key findings, and conclusion of the study. Critical appraisal of the study's validity and relevance is also a key skill in evidence-based medicine. IVS Can you provide a summary of the article entitled "Ulnar trochlear notch articular surface has three morphological patterns: a neglected major anatomical feature", which was published in 2019 in the surgical and radiologic anatomy journal  [3] , for a medical student? ChatGPT; I'm sorry for any confusion, but as an AI model developed by OpenAI, I don't have direct access to my training data or know where it comes from. However, I was trained on a mixture of licensed data, data created by human trainers, and publicly available data. I have not been directly trained on specific databases or have the ability to access or retrieve personal data unless it has been shared with me in the course of our conversation.
I am designed to respect user privacy and confidentiality. My primary function is to provide information and answer questions to the best of my knowledge and abilities. If you have any concerns about privacy or data security, feel free to let me know, and I will do my best to address them. Unfortunately, I don't have the ability to access databases, subscription sites, confidential information, or proprietary resources, so I can't provide a summary of the specific article from "Surgical and Radiologic Anatomy" you mentioned. I don't have the ability to access the internet, retrieve, or know specific documents or sources. I recommend reading the abstract for a summary of the paper. The introduction will give you background information and the context of the study, the methods section will tell you how the study was conducted, the results section will present the findings, and the discussion and conclusion sections will interpret the results and suggest further research.
Discussion-commentary In the beginning, the chatbot acknowledges that it may not always provide accurate replies and is meant to be an AE supplement. When discussing how generative language models can assist medical students in AE, ChatGPT mentions interactive learning tools like simulated doctor-patient scenarios. While these can be useful for ME, their relevance to AE may be limited as anatomy is typically taught early on, before students have gained experience in patient interaction, diagnostic thinking, and reviewing patient records. It seems the chatbot is capable of providing concise chapter summaries in bullet point format, which can aid students in their studies. It could be beneficial for a student to have the option of creating multiple-choice quizzes or matching questions with different levels of difficulty while preparing for an exam.
ChatGPT and the study's limitations A notable drawback of the chatbot is that it cannot provide images. Instead, it directs users to anatomy textbooks and digital platforms for illustrations. This is a significant limitation as images are crucial for understanding anatomy. Additionally, since the chatbot does not provide citations in its replies, its statements cannot be considered evidence-based. Another limitation is that the chatbot, ChatGPT-4, lacks  1 3  knowledge of research data published after its last update in September 2021. Despite these limitations, ChatGPT-4 acknowledges its weaknesses and recognizes the potential for improvement. It is important to note that the present study only explored limited anatomical topics to assess the chatbot's potential impact on ME and MR. Therefore, the accuracy of the chatbot's replies may vary if users ask about different anatomical structures.
In this approach, machine-learning models are exploited to recognize human activities, and ontology and commonsense reasoning are used to capture context and recognize normal and abnormal human behavior. The proposed framework is evaluated using two datasets, namely Orange4Home  [25]  and UCI HAR  [26]  datasets in terms of precision, recall, F-measure, and accuracy. The contributions of this paper are as follows: • Development of a hybrid and context-aware approach for recognizing human behavior, which incorporates context attributes using machinelearning models, HAT ontology, and PASP. • Development of a hybrid and context-aware approach for detecting abnormal human behavior, which considers human behavior context and handles uncertainty in activity predictions and abnormal human behavior rules using probabilistic reasoning.
• Evaluation of the proposed framework for recognizing human activities, human behaviors, and abnormal human behaviors using two datasets, namely Orange4Home  [25]  and UCI HAR  [26]  datasets, based on precision, recall, F-measure, and accuracy. The remainder of the paper is structured as follows: In section II, we review related works in the field of Human Activity Recognition (HAR), human behavior, and abnormal human behavior recognition. Section III provides an overview of the datasets used in this study. In section IV, we detail the proposed framework. Section V presents the results of the experiments conducted to evaluate the framework's performance. Lastly, in section VI, we discuss the conclusions and future perspectives of our research.
The main advantage of these approaches is their ability in knowledge representation and in verifying the correctness of properties in their axiomatizations  [47]. In  [48] , an Event Calculus (EC)-based framework is proposed for modeling high-level activities, consisting of more than one event performed consecutively or simultaneously. In this framework, sensor data are analyzed to recognize the occurrence of events; the recognized events are then exploited by a reasoning engine for identifying highlevel activities, such as having breakfast. In  [49]  and  [50] , EC is exploited to recognize long-term activities. Ontologies have been broadly exploited to infer human activities due to their powerful semantic representation of real-world and reasoning capabilities  [51]. In  [52] , an ontology is used to model sensors and activities as classes considering object-based and location-based concepts of activities; these concepts are exploited by reasoning approaches to recognize human activities.