Building and analyzing metacells in single-cell genomics data.

The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).

Training machine learning potentials for reactive systems: A Colab tutorial on basic models.

In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field - the training of system-specific MLPs for reactive systems - with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel-based (using Gaussian process regression, GPR) models by fitting the two-dimensional Müller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction.

Principles of artificial intelligence in radiooncology.

PURPOSE: In the rapidly expanding field of artificial intelligence (AI) there is a wealth of literature detailing the myriad applications of AI, particularly in the realm of deep learning. However, a review that elucidates the technical principles of deep learning as relevant to radiation oncology in an easily understandable manner is still notably lacking. This paper aims to fill this gap by providing a comprehensive guide to the principles of deep learning that is specifically tailored toward radiation oncology. METHODS: In light of the extensive variety of AI methodologies, this review selectively concentrates on the specific domain of deep learning. It emphasizes the principal categories of deep learning models and delineates the methodologies for training these models effectively. RESULTS: This review initially delineates the distinctions between AI and deep learning as well as between supervised and unsupervised learning. Subsequently, it elucidates the fundamental principles of major deep learning models, encompassing multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), diffusion-based generative models, and reinforcement learning. For each category, it presents representative networks alongside their specific applications in radiation oncology. Moreover, the review outlines critical factors essential for training deep learning models, such as data preprocessing, loss functions, optimizers, and other pivotal training parameters including learning rate and batch size. CONCLUSION: This review provides a comprehensive overview of deep learning principles tailored toward radiation oncology. It aims to enhance the understanding of AI-based research and software applications, thereby bridging the gap between complex technological concepts and clinical practice in radiation oncology.

A framework for integrating evidence to assess hazards and risk.

To accurately characterize human health hazards, human, animal, and mechanistic data must be integrated and the relevance to the research question of all three lines of evidence must be considered. Mechanistic data are often critical to the full integration of animal and human data and to characterizing relevance and uncertainty. This novel evidence integration framework (EIF) provides a method for synthesizing data from comprehensive, systematic, quality-based assessments of the epidemiological and toxicological literature, including in vivo and in vitro mechanistic studies. It organizes data according to both the observed human health effects and the mechanism of action of the chemical, providing a method to support evidence synthesis. The disease-based component uses the evidence of human health outcomes studied in the best quality epidemiological literature to organize the toxicological data according to authors' stated purpose, with the pathophysiology of the disease determining the potential relevance of the toxicological data. The mechanism-based component organizes the data based on the proposed mechanisms of effect and data supporting events leading to each endpoint, with the epidemiological data potentially providing corroborating information. The EIF includes a method to cross-classify and describe the concordance of the data, and to characterize its uncertainty. At times, the two methods of organizing the data may lead to different conclusions. This facilitates identification of knowledge gaps and shows the impact of uncertainties on the strength of causal inference.

Parameter estimation and forecasting with quantified uncertainty for ordinary differential equation models using QuantDiffForecast: A MATLAB toolbox and tutorial.

Mathematical models based on systems of ordinary differential equations (ODEs) are frequently applied in various scientific fields to assess hypotheses, estimate key model parameters, and generate predictions about the system's state. To support their application, we present a comprehensive, easy-to-use, and flexible MATLAB toolbox, QuantDiffForecast, and associated tutorial to estimate parameters and generate short-term forecasts with quantified uncertainty from dynamical models based on systems of ODEs. We provide software ( https://github.com/gchowell/paramEstimation_forecasting_ODEmodels/) and detailed guidance on estimating parameters and forecasting time-series trajectories that are characterized using ODEs with quantified uncertainty through a parametric bootstrapping approach. It includes functions that allow the user to infer model parameters and assess forecasting performance for different ODE models specified by the user, using different estimation methods and error structures in the data. The tutorial is intended for a diverse audience, including students training in dynamic systems, and will be broadly applicable to estimate parameters and generate forecasts from models based on ODEs. The functions included in the toolbox are illustrated using epidemic models with varying levels of complexity applied to data from the 1918 influenza pandemic in San Francisco. A tutorial video that demonstrates the functionality of the toolbox is included.

Gaussian process emulation to improve efficiency of computationally intensive multidisease models: a practical tutorial with adaptable R code.

BACKGROUND: The rapidly growing burden of non-communicable diseases (NCDs) among people living with HIV in sub-Saharan Africa (SSA) has expanded the number of multidisease models predicting future care needs and health system priorities. Usefulness of these models depends on their ability to replicate real-life data and be readily understood and applied by public health decision-makers; yet existing simulation models of HIV comorbidities are computationally expensive and require large numbers of parameters and long run times, which hinders their utility in resource-constrained settings. METHODS: We present a novel, user-friendly emulator that can efficiently approximate complex simulators of long-term HIV and NCD outcomes in Africa. We describe how to implement the emulator via a tutorial based on publicly available data from Kenya. Emulator parameters relating to incidence and prevalence of HIV, hypertension and depression were derived from our own agent-based simulation model and other published literature. Gaussian processes were used to fit the emulator to simulator estimates, assuming presence of noise for design points. Bayesian posterior predictive checks and leave-one-out cross validation confirmed the emulator's descriptive accuracy. RESULTS: In this example, our emulator resulted in a 13-fold (95% Confidence Interval (CI): 8-22) improvement in computing time compared to that of more complex chronic disease simulation models. One emulator run took 3.00 seconds (95% CI: 1.65-5.28) on a 64-bit operating system laptop with 8.00 gigabytes (GB) of Random Access Memory (RAM), compared to > 11 hours for 1000 simulator runs on a high-performance computing cluster with 1500 GBs of RAM. Pareto k estimates were < 0.70 for all emulations, which demonstrates sufficient predictive accuracy of the emulator. CONCLUSIONS: The emulator presented in this tutorial offers a practical and flexible modelling tool that can help inform health policy-making in countries with a generalized HIV epidemic and growing NCD burden. Future emulator applications could be used to forecast the changing burden of HIV, hypertension and depression over an extended (> 10 year) period, estimate longer-term prevalence of other co-occurring conditions (e.g., postpartum depression among women living with HIV), and project the impact of nationally-prioritized interventions such as national health insurance schemes and differentiated care models.

DNA nanostructure decoration: a how-to tutorial.

DNA Nanotechnology is being applied to multiple research fields. The functionality of DNA nanostructures is significantly enhanced by decorating them with nanoscale moieties including: proteins, metallic nanoparticles, quantum dots, and chromophores. Decoration is a complex process and developing protocols for reliable attachment routinely requires extensive trial and error. Additionally, the granular nature of scientific communication makes it difficult to discern general principles in DNA nanostructure decoration. This tutorial is a guidebook designed to minimize experimental bottlenecks and avoid dead-ends for those wishing to decorate DNA nanostructures. We supplement the reference material on available technical tools and procedures with a conceptual framework required to make efficient and effective decisions in the lab. Together these resources should aid both the novice and the expert to develop and execute a rapid, reliable decoration protocols.

Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability.

Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy - a critical period of rapid brain development and plasticity - microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.

Using structural equation modeling to investigate students' satisfaction with an undergraduate tutorial system.

BACKGROUND: The undergraduate tutorial system (UTS) is a crucial measure in China for adhering to the principle of prioritizing foundational education, innovating the undergraduate talent training mode, and building a powerful country of higher education. This study investigated undergraduate students' satisfaction with UTS and the influencing factors, aiming to promote the healthy and sustainable development of UTS and provide practical implications and suggestions for universities. METHODS: Based on relevant theories, we conducted a survey study and leveraged structural equation modeling to assess students' satisfaction with UTS and explore the influencing factors. RESULTS: Our Pearson correlation analysis showed that students' satisfaction with mentors was positively correlated with dimensions such as humanistic care (r = 0.844, P < 0.05), mentor assistance (r = 0.906, P < 0.05), and mentor-student communication (r = 0.908, P < 0.05). Path analysis showed that mentor-student communication (ß = 0.486, P < 0.01), mentor assistance (ß = 0.228, P < 0.05), humanistic care (ß = 0.105, P < 0.05) were positive factors affecting students' satisfaction with mentors, while satisfaction with mentors (ß = 0.923, P < 0.01) had a positive impact on students' satisfaction with UTS. Students' satisfaction with mentors explained 73.4% of the variation in students' satisfaction with UTS, indicating that satisfaction with mentors was an important intermediary variable of UTS students. CONCLUSION: The sustainable implementation of UTS requires the effort to improve student satisfaction, and the breakthrough of strengthening the targeted mentorship in "transmitting wisdom, imparting knowledge, and resolving doubts" for students. Efforts should also be devoted to fostering students' comprehensive skills and better serving the cultivation of talents in the new era.

Flipped and Peer-Assisted teaching: a new model in virtual anatomy education.

INTRODUCTION: In response to the COVID-19 crisis, this study aimed to introduce a new virtual teaching model for anatomy education that combines Peer-Assisted Learning (PAL) and flipped classrooms, aligning with constructivist principles. METHOD: The Flipped Peer Assisted (FPA) method was implemented in a virtual neuroanatomy course for second-year medical students at Birjand University of Medical Sciences via a descriptive study. The method involved small groups of PAL, with peer learning serving as educational assistants and the teacher acting as a facilitator. Educational content was uploaded to the university's learning management system (LMS). The opinion of medical students regarding the teaching method were evaluated using a 15-item questionnaire on a five-point Likert scale. RESULTS: A total of 210 students participated in the instruction using the FPA method. The analysis of students' scores revealed an average score of 26.75 ± 3.67 on the 30-point test. According to student feedback, this teaching method effectively motivated students to study, enhanced teamwork and communication skills, transformed their perspective on the anatomy course, provided opportunities for formative assessment and feedback, and demonstrated the teacher's dedication to education. CONCLUSION: The FPA model demonstrates its effectiveness in transforming traditional classroom teaching and fostering teaching and learning in virtual environments, particularly during pandemics like COVID-19. This model holds promise for enhancing anatomy education in challenging circumstances.

A case study: lessons learned from online tutorial to improve practice readiness for family medicine residents in Palestine.

BACKGROUND: Geopolitical and socioeconomic challenges limit faculty development and clinical teaching in Palestine and many other developing countries. The first, and still only, Family Medicine (FM) residency program is a four-year program based out of An-Najah University in the West Bank. Training in primary care clinics occurs in the final two years and there are many challenges to adequate supervision in the clinical setting that were exacerbated during the pandemic. To improve the readiness for practice skills of 13 Palestinian FM residents a three-month tutorial program was organized in 2020. A nongovernmental organization (NGO) that has worked to support Family Medicine development in the region engaged experienced British and American General Practitioners trained as tutors to offer online tutorials. We examined the program as a case study to understand the factors that facilitated or impaired a positive virtual learning environment in a middle/low income country. METHODS: The tutors and residents were divided into groups and met virtually between June and September 2020. Evaluations and session reports collected during the program, the text of an online chat, and responses to an online survey two years later were collected. Using thematic analysis techniques, we evaluated the value for the residents at the time and two years later and identified factors that facilitated or impaired a positive virtual learning environment. RESULTS: Themes of knowledge, skills, attitudes, cultural disconnects, and tutorial logistics emerged. The group with the most stable tutor pairing, including one Arabic-speaker familiar with the context, was the most engaged. The all-female group formed a chat group to share real-time case questions during clinical practice and focused on skills (e.g. conducting a thorough medication review) and attitudes (e.g. open to sharing and discussing uncertainties). Other groups were less cohesive. CONCLUSIONS: Transnational tutorials that focused on clinical thinking and decision-making skills were most successful when the tutorial pair was stable, offered familiarity with the language and addressed cultural differences. Intrinsic factors such as lacking the motivation to participate and extrinsic factors such as unstable internet and rolling electric cuts, and clinical structures that made applying new skills challenging were more difficult to address but must be considered.

Signal Processing for the Condition-Based Maintenance of Rotating Machines via Vibration Analysis: A Tutorial.

One of the common methods for implementing the condition-based maintenance of rotating machinery is vibration analysis. This tutorial describes some of the important signal processing methods existing in the field, which are based on a profound understanding of the component's physical behavior. Furthermore, this tutorial provides Python and MATLAB code examples to demonstrate these methods alongside explanatory videos. The goal of this article is to serve as a practical tutorial, enabling interested individuals with a background in signal processing to quickly learn the important principles of condition-based maintenance of rotating machinery using vibration analysis.

Interactive Pathology Tutorial in Neoplastic Hematology Disorders for Medical Hematology-Oncology Fellows.

Hematology-oncology (HO) fellows receive limited instruction in the process of establishing a diagnosis for hematologic neoplasms, and learning neoplastic hematology often occurs in limited encounters. In the current study, we developed a web-based interactive pathology tutorial in neoplastic hematologic disorders for HO fellows to work up simulated cases and establish the diagnosis. An online system ("Pathology Playground") was utilized to load case materials including microscopic images and ancillary studies. Twelve high-yield simulated cases of common leukemias and lymphoma were included. At the beginning of each case, trainees review the clinical history and slide images, and then, they are given the option to request additional pathology work-up. Based on the results, they can enter their diagnostic impression. If the diagnosis is correct, the user is shown a short educational presentation. If the diagnosis is not correct, the user gets notified by the message "Incorrect." The tutorial was integrated in the educational curriculum of our HO fellowship program, and bimonthly teaching sessions were held to review two cases each time. During the sessions, trainees request ancillary studies to complete the diagnostic work-up using the software and interpret the findings. As the case is being worked up by the trainee, the hematopathologists and HO fellowship program director discuss the findings, the appropriate work-up tools, and the implications on management. All of our six HO fellows attended the sessions, and a survey from the trainees showed high ease of use of the system and they viewed it as a very useful educational tool. A pre-test and post-test were administered for one of the sessions, and the result showed improvement in the average from 62 to 73%. Expanding the use of this online interactive tutorial and incorporating additional cases would enhance its value as a learning resource.

The pivotal role of clinical tutorial teaching in knowledge construction.

Students' new knowledge is gradually built up in the context of the task for which it is required and consolidated by applying it to clinical cases. As students see more and more clinical cases the knowledge emerges from an associative mesh of different levels of understanding. During tutorial clinical teaching, residents should be gradually exposed to an increasing range of real-world learning tasks and increasing levels of complexity. This exposure allows them to gradually develop shortcuts in the retrieval of their knowledge. This commentary provides a rationale for the construction of knowledge and the pivotal role that clinical tutorial teaching plays in this task.

MICROSTATELAB: The EEGLAB Toolbox for Resting-State Microstate Analysis.

Microstate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and detect outlier maps, (iii) visualize, sort, and label individual, mean, and grand mean microstate maps according to published maps, (iv) compare topographical similarities of group and grand mean microstate maps and quantify shared variances, (v) obtain the temporal dynamics of the microstate classes in individual EEGs, (vi) export quantifications of these temporal dynamics of the microstates for statistical tests, and finally, (vii) test for topographical differences between groups and conditions using topographic analysis of variance (TANOVA). Here, we introduce the toolbox in a step-by-step tutorial, using a sample dataset of 34 resting-state EEG recordings that are publicly available to follow along with this tutorial. The goals of this manuscript are (a) to provide a standardized, freely available toolbox for resting-state microstate analysis to the scientific community, (b) to allow researchers to use best practices for microstate analysis by following a step-by-step tutorial, and (c) to improve the methodological standards of microstate research by providing previously unavailable functions and recommendations on critical decisions required in microstate analyses.

Using Joinpoint regression for drug utilization research: Tutorial and case study of prescription opioid use in the United States.

PURPOSE: Drug utilization researchers are often interested in evaluating prescribing and medication use patterns and trends over a specified period of time. Joinpoint regression is a useful methodology to identify any deviations in secular trends without a preconceived notion of where these break points might occur. This article provides a tutorial on the use of joinpoint regression, within Joinpoint software, for the analysis of drug utilization data. METHODS: The statistical considerations for whether a joinpoint regression analytical technique is a suitable approach are discussed. Then, we offer a tutorial as an introduction on conducting joinpoint regression (within Joinpoint software) through a step-by-step application, which is a case study developed using opioid prescribing data from the United States. Data were obtained from public files available through the Centers for Disease Control and Prevention from 2006 to 2018. The tutorial provides parameters and sample data needed to replicate the case study and it concludes with general considerations for the reporting of results using joinpoint regression in drug utilization research. RESULTS: The case study evaluated the trend of opioid prescribing in the United States from 2006 to 2018, where time points of significant variation (one in 2012 and another in 2016) are detected and interpreted. CONCLUSIONS: Joinpoint regression is a helpful methodology for drug utilization for the purposes of conducting descriptive analyses. This tool also assists with corroborating assumptions and identifying parameters for fitting other models such as interrupted time series. The technique and accompanying software are user-friendly; however, researchers interested in using joinpoint regression should exercise caution and follow best practices for correct measurement of drug utilization.

Total introital obliteration as a consequence of lichen sclerosus: a rare cause of urinary retention.

INTRODUCTION AND HYPOTHESIS: Lichen sclerosus is a chronic inflammatory dermatitis, with a predilection for the anogenital area. In later stages, lichen sclerosus may develop into widespread scarring, and occasionally leading to severe introital stenosis and urinary retention. Our video is aimed at presenting a case of surgical management of lichen sclerosus-related introital stenosis determining urinary retention. METHODS: An 82-year-old woman was evaluated for almost complete urinary retention, with concomitant continuous enuretic urinary leakage all day and night and recurrent urinary tract infection symptoms. The gynecological evaluation demonstrated a complete introital obliteration, without obvious communications for urine passing. After proper informed consent, the patient was admitted for vulvo-perineoplasty. RESULTS: The featured procedure was completed in 25 min and blood loss was negligible. No surgical complications were observed. On postoperative day 1, the patient was successfully discharged home with topical steroid treatment. Histological examination confirmed typical features of lichen sclerosus pathology. At follow-up visits the patient was asymptomatic and examination confirmed persistence of introital patency. CONCLUSIONS: The featured video shows a vulvo-perineoplasty performed in a patient with lichen sclerosus-related complete introital obliteration and urinary retention. The procedure was successful in obtaining anatomical repair and relieving urinary symptoms.

Surgical management of rectovaginal fistula after stapled transanal rectal resection for prolapsed hemorrhoids.

INTRODUCTION AND HYPOTHESIS: Rectovaginal fistula is an epithelium-lined direct communication route between the vagina and the rectum. The gold standard of fistula management is surgical treatment. Rectovaginal fistula after stapled transanal rectal resection (STARR) may be challenging to treat, due to the extensive scarring, the local ischemia, and the risk of rectal stenosis. We aimed to present a case of iatrogenic rectovaginal fistula after STARR that was successfully treated with a transvaginal primary layered repair and bowel diversion. METHODS: A 38-year-old woman was referred to our division for continuous fecal discharge through her vagina that developed a few days after she had a STARR for prolapsed hemorrhoids. Clinical examination revealed a 2.5 cm-wide direct communication between the vagina and rectum. After proper counseling, the patient was admitted to transvaginal layered repair and temporary laparoscopic bowel diversion RESULTS: No surgical complications were observed. The patient was successfully discharged home on postoperative day 3. Bowel diversion was reversed after 2 months. At the current follow-up (6 months), the patient is asymptomatic and without recurrence. CONCLUSIONS: The procedure was successful in obtaining anatomical repair and relieving symptoms. This approach represents a valid procedure for the surgical management of this severe condition.

Pelvic pain after laparoscopic lateral suspension for pelvic organ prolapse: an unrecognized problem?

INTRODUCTION AND HYPOTHESIS: The laparoscopic lateral suspension (LLS) represents an alternative mesh procedure to avoid the dissection at the promontory. However, mesh-related complications such as chronic pelvic pain, dyspareunia, and bladder pain are emerging. The present study is aimed to present a video case report and describe a small case series of patients referred to our center for chronic pelvic pain after LLS. METHODS: A surgical video of the management of a 50-year-old woman with chronic abdominal and pelvic pain, dyspareunia, and recurrent urinary tract infection (UTIs) after uterus sparing LLS is provided. Moreover, we performed a retrospective chart review of similar cases in our institution. RESULTS: The featured procedure was completed without complications and was successful in obtaining symptom relief. Between 2018 and 2022 five patients underwent total or subtotal mesh removal for pain-related symptoms after LLS. At the median follow-up of 24 months, all patients were free from pain, but two (40%) required reoperation for prolapse recurrence CONCLUSIONS: Our experience suggests that LLS involves a certain risk of chronic pelvic pain, which may be challenging to manage and require surgical treatment.

An Interdisciplinary Tutorial: A Self-Healing Soft Finger with Embedded Sensor.

In the field of soft robotics, knowledge of material science is becoming more and more important. However, many researchers have a background in only one of both domains. To aid the understanding of the other domain, this tutorial describes the complete process from polymer synthesis over fabrication to testing of a soft finger. Enough background is provided during the tutorial such that researchers from both fields can understand and sharpen their knowledge. Self-healing polymers are used in this tutorial, showing that these polymers that were once a specialty, have become accessible for broader use. The use of self-healing polymers allows soft robots to recover from fatal damage, as shown in this tutorial, which increases their lifespan significantly.