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Background The objective was to summarize the methodology used to reach consensus for recommended minimum data elements that should be collected and reported when conducting injury surveillance research in military settings. This paper summarizes the methodology used to develop the international Minimum Data Elements for surveillance and Reporting of Musculoskeletal Injuries in the MILitary (ROMMIL) statement. Methods A Delphi methodology was employed to reach consensus for minimum reporting elements. Preliminary steps included conducting a literature review and surveying a convenience sample of military stakeholders to 1) identify barriers and facilitators of military musculoskeletal injury (MSKI) prevention programs, 2) identify relevant knowledge/information gaps and 3) establish future research priorities. The team then led a sequential three-round Delphi consensus survey, including relevant stakeholders from militaries around the world, and then conducted asynchronous mixed knowledge user meeting to explore level of agreement among subject matter experts. Knowledge users, including former and current military service members, civil servant practitioners, and global-wide subject matter experts having experience with policy, execution, or clinical investigation of MSKI mitigation programs, MSKI diagnoses, and MSKI risk factors in military settings. For each round, participants scored each question on a Likert scale of 1-5. Scores ranged from No Importance (1) to Strong Importance (5). Results Literature review and surveys helped informed the scope of potential variables to vote on. Three rounds were necessary to reach minimum consensus. Ninety-five, 65 and 42 respondents participated in the first, second and third rounds of the Delphi consensus, respectively. Ultimately, consensus recommendations emerged consisting of one data principle and 33 minimum data elements. Conclusions Achieving consensus across relevant stakeholders representing military organizations globally can be challenging. This paper details the methodology employed to reach consensus for a core minimum data elements checklist for conducting MSKI research in military settings and improve data harmonization and scalability efforts. These methods can be used as a resource to assist in future consensus endeavors of similar nature.

Background: Many randomized trials measure a continuous outcome simultaneously at baseline and after taking the drug. For a single continuous post-treatment outcome, the sample size calculation is simple, but if there are assessments at multiple time points post-treatment then this longitudinal data may give more insights by analyzing the data using the repeated measures method. Also, if the sample size is calculated using the single time-point method for longitudinal data, it may lead to a larger than required sample size, increasing the cost and time. Methods: In this research, an effort is made to determine the size of the sample for repeated measures case and then compared with the single post-baseline case. The sample sizes were examined under different scenarios for the continuous type of response variable. Under Mean contrast and Diff contrast the sample sizes were calculated with different correlations. These two scenarios were again examined under compound symmetry as well as Auto regressive of order 1 type of correlation structure in longitudinal data. The graphical presentation is given for better visualization of the scenarios. Results: Sample size required for highly correlated longitudinal data using multi timepoint sample size derivation method led to much smaller sample size requirement as compared to single timepoint sample size calculation method. Conclusions: This study will help researchers to make better decisions in choosing the right method for sample size determination which may reduce the time and cost of carrying out the experiment. Also, we must carefully assess which method to go with when the correlation is weak. More complex correlation structures are not studied in this article but can be studied in the same fashion.

Background Digital elevation models (DEMs) are important in many fields, such as geomatics and water conservation in mountainous areas. Geomorphic feature lines are necessary for topography interpolation and computation from DEMs. Methods Instead of a parameter space, we propose a novel automatic extraction of geomorphic feature lines in the feature space from discrete airborne light detection and ranging (LiDAR) data by the tensor voting method (TVM), which was originally developed for image data. A tensor field for discrete airborne LiDAR points was first established, and then, utilizing the TVM, a new geometric feature metric of data, the line feature strength, was captured. A practical line-growing method based on the local maxima line feature strength is proposed in this study. Results Compared with general line growing, which is based on a certain threshold, our line growing method is quite effective, particularly for the extraction of primary and minor ridge and valley lines in mountainous areas. Conclusions The method presented in this paper is fast and automated, and can furnish operators with a wealth of detailed information about minor line features. This enables the extraction of ridge and valley lines that are tailored to specific requirements. Undoubtedly, the method developed here can be generalized to a large amount of LiDAR data.

Background Diffusion-weighted MRI (DWI) offers a non-invasive approach to detect tumors based on water mobility differences from surrounding tissue. However, reliably discriminating malignancies from benign adipose remains challenging, especially in anatomical regions with abundant fat. The biophysical similarities between tumors and lipid signals create signal ambiguities that limit diagnostic accuracy using conventional reconstruction techniques. Methods We propose a novel low-rank reconstruction framework that combines accelerated diffusion data acquisition, structured low-rank regularization, and deep learning-assisted radiomic analysis to enhance fat-tumor discrimination in DWI. Simultaneous multi-slice imaging and controlled aliasing enable high spatiotemporal resolution while maintaining feasible scan times. A data-driven annihilating filter kernel is then learned from the undersampled data, imposing implicit low-rank constraints to suppress confounding fat signals while retaining tumor texture details during k-space reconstruction. Subsequent radiomic analysis extracts morphological imaging biomarkers from the reconstructed volumes to identify distinctive tumor signatures. Results Comprehensive validation on clinical DWI datasets demonstrates the improved fat-tumor discrimination capability of the proposed framework compared to conventional techniques. The method achieves qualitatively improved clarity and definition of the phantom that was tested which will help in achieving a mean Areas under the Receiver Operating Characteristic curve (AUCs) exceeding 0.80 for distinguishing malignant lesions from adipose tissue. Case studies illustrate how better signal specificity enables more confident clinical decisions. Conclusions The integrated low-rank reconstruction and radiomic analysis framework offers a promising solution to the longstanding problem of fat-tumor ambiguity in diffusion MRI. By unleashing the full diagnostic potential of DWI, this methodology can enhance non-invasive cancer screening and monitoring across diverse patient populations and anatomical regions.

Background: Phlebotomy is a medical procedure that is performed frequently in the blood collection activities of medical institutions. The procedure involves close interaction with different types of patients—some of whom are cooperative and others, who, for many reasons, are not (for example, patients who have a fear of needles). Blood extraction is an essential skill in several medical specialties, such as in laboratory sciences. Lesson planning in phlebotomy education is mainly focused on procedural skills, and very little attention is given towards teaching communication skills despite the close patient interaction in phlebotomy. In this paper, I propose a lesson plan for teaching communication skills to medical laboratory sciences and nursing students based on Gagne’s instructional design. Methods: The training session included two main parts: training session using Gange’s instructional design and at the end of the session, the participants were surveyed for the effectiveness of the training session. Results: 17 participants were included in the study. Overall, the majority of the participants were highly satisfied with the effectiveness of the training session in teaching communication skills with all seven survey questions receiving a mean score of 4.58 on a Likert scale of 1-5. Conclusion: We demonstrated the effectiveness of Gange’s instructional beyond theoretical lesson planning to teach communication skills through role-play in phlebotomy education.

Background There are insufficient in vitro bone models that accommodate long-term culture of osteoblasts and support their differentiation to osteocytes. The increased demand for effective therapies for bone diseases, and the ethical requirement to replace animals in research, warrants the development of such models. Here we present an in-depth protocol to prepare, create and maintain three-dimensional, in vitro, self-structuring bone models that support osteocytogenesis and long-term osteoblast survival (>1 year). Methods Osteoblastic cells are seeded on a fibrin hydrogel, cast between two beta-tricalcium phosphate anchors. Analytical methods optimised for these self-structuring bone model (SSBM) constructs, including RT-qPCR, immunofluorescence staining and XRF, are described in detail. Results Over time, the cells restructure and replace the initial matrix with a collagen-rich, mineralising one; and demonstrate differentiation towards osteocytes within 12 weeks of culture. Conclusions Whilst optimised using a secondary human cell line (hFOB 1.19), this protocol readily accommodates osteoblasts from other species (rat and mouse) and origins (primary and secondary). This simple, straightforward method creates reproducible in vitro bone models that are responsive to exogenous stimuli, offering a versatile platform for conducting preclinical translatable research studies.

Background Expert consensus methods are regularly used in natural, social, and life sciences. This article explores the potential of applying these methods more frequently in humanities research. Methods The authors reviewed literature and applied the philosophical methods of conceptual analysis and conceptual engineering. Results This article identifies and describes six main elements of expert consensus methods. It also provides an overview of the different types of expert consensus methods regularly used in the natural, social, and life sciences: Delphi studies, nominal groups, consensus conferences, and Glaser’s state of the art method. Subsequently, each of these types is illustrated by an example from the sciences. The article also presents the potential of and objections to the application of expert consensus methods there. It gives four examples of expert consensus methods that were applied in humanities research, also presented in line with the six elements. Conclusions The comparisons and categorization show that, as in the natural, social, and life sciences, expert consensus methods in the humanities can in some instances potentially accelerate the epistemic process and enhance transparency, replicability, diversity, and fair processes. Nevertheless, expert consensus methods need to be fine-tuned to do justice to the unique nature and approaches of the humanities and therefore further research is needed.

Fish species are commonly used as experimental models in the laboratory. DNA is routinely collected from these animals to permit identification of their genotype. The current standard procedure to sample DNA is fin clipping, which involves anaesthetising individuals and removing a portion of the caudal fin. While fin clipping reliably generates good quality DNA samples for downstream applications, there is evidence that it can alter health and welfare, and impact the fish’s behaviour. This in turn can result in greater variation in the data collected. In a recent study we adapted a skin swabbing protocol to collect DNA from small-bodied fish, including sticklebacks and zebrafish, without the use of analgesics, anaesthetics or sharp instruments. A rayon-tipped swab was used to collect mucus from the flank of the fish, which was then used for DNA extraction. We subsequently demonstrated that compared to fin clipping, skin swabbing triggered fewer changes in stress axis activation and behaviour. We also found that gene expression and behaviour data collected from swabbed fish were less variable than similar data collected from fish that had been fin clipped. This potentially allows smaller sample sizes in experimental groups to be used after skin swabbing, thereby reducing animal use. Here we provide a detailed protocol explaining how to collect DNA samples from small laboratory fish using skin swabs.

Background Establishment of a systematic way of measurement and utilization of indicators for improvement is one of the most challenging issues in monitoring and evaluation of indicators in healthcare settings. In realizing these fundamental challenges, we designed a monitoring and evaluation system incorporating a hospital-wide quality variance report (QVR) system using linked Microsoft Excel® spreadsheets on Microsoft SharePoint®. Methods Indicators were determined at the departmental/unit level in line with the institutional goals, departmental functions, quality expectations, inputs/outputs, clinical priorities, compliance to policies/procedures/protocols/guidelines/pathways as well as in response to gaps in service delivery picked during root cause analyses. The sample design was determined in accordance with the characteristics of the population. Drawing of sample units was done using a simple random sampling technique without replacement or systematic random sampling. The indicator’s monitoring was enhanced visually by allocating colour codes based on performance across the months and quarters. The action plan tab consisted of a platform that aids in documenting corrective actions arising from the performance reviews. Results and discussion The QVR reporting system ensured a standardized format of monitoring throughout the institution with a reduced turnaround time from data collection to analysis. Further, continuity of the monitoring and evaluation (M&E) system was guaranteed even if an individual left the institution. The analysis of the QVR allowed hospital-wide trending on cross-cutting indicators with consequent ease of communication to multiple stakeholders. The automation has saved time and increased accuracy which has enhanced credible engagements during quality meetings. Conclusions Use of this system greatly enhanced quality performance monitoring in the hospital, identification of major bottlenecks that warranted hospital-wide projects or departmental-level projects. The QVR system enhanced the efficiency and accuracy of quality monitoring from data collection through to performance reviews. The QVR structure allows for customized development of an M&E database application software.

We develop a thermodynamic model describing the binding of RNA binding proteins (RBP) to oligomers in vitro. We apply expectation-maximization to infer the specificity of RBPs, represented as position-specific weight matrices (PWMs), by maximizing the likelihood of RNA Bind’n Seq data from the ENCODE project. Analyzing these public data we find sequence motifs that can partly explain the data for more than half of the studied 111 RBPs, and for 48 of the proteins these motifs are consistent with the known specificity. Our code is publicly available, facilitating analysis of RBP binding data.

Background Researchers are focusing their emphasis on quick and real-time healthcare and monitoring systems because of the contemporary modern world’s rapid technological improvements. One of the best options is smart healthcare, which uses a variety of on-body and off-body sensors and gadgets to monitor patients’ health and exchange data with hospitals and healthcare professionals in real time. Utilizing the primary user (PU) spectrum, cognitive radio (CR) can be highly useful for efficient and intelligent healthcare systems to send and receive patient health data. Methods In this work, we propose a method that combines energy detection (ED) and cyclostationary (CS) spectrum sensing (SS) algorithms. This method was used to test spectrum sensing in CR-based smart healthcare systems. The proposed ED-CS in cognitive radio systems improves the precision of the spectrum sensing. Owing to its straightforward implementation, ED is initially used to identify the idle spectrum. If the ED cannot find the idle spectrum, the signals are found using CS-SS, which uses the cyclic statistical properties of the signals to separate the main users from the interference. Results In the simulation analysis, the probability of detection (Pd), probability of a false alarm (Pfa), power spectral density (PSD), and bit error rate (BER) of the proposed ED-CS is compared to those of the traditional Matched Filter (MF), ED, and CS. Conclusions The results indicate that the suggested strategy improves the performance of the framework, making it more appropriate for smart healthcare applications.

Background Research and researchers are heavily evaluated, and over the past decade it has become widely acknowledged that the consequences of evaluating the research enterprise and particularly individual researchers are considerable. This has resulted in the publishing of several guidelines and principles to support moving towards more responsible research assessment (RRA). To ensure that research evaluation is meaningful, responsible, and effective the International Network of Research Management Societies (INORMS) Research Evaluation Group created the SCOPE framework enabling evaluators to deliver on existing principles of RRA. SCOPE bridges the gap between principles and their implementation by providing a structured five-stage framework by which evaluations can be designed and implemented, as well as evaluated. Methods SCOPE is a step-by-step process designed to help plan, design, and conduct research evaluations as well as check effectiveness of existing evaluations. In this article, four case studies are presented to show how SCOPE has been used in practice to provide value-based research evaluation. Results This article situates SCOPE within the international work towards more meaningful and robust research evaluation practices and shows through the four case studies how it can be used by different organisations to develop evaluations at different levels of granularity and in different settings. Conclusions The article demonstrates that the SCOPE framework is rooted firmly in the existing literature. In addition, it is argued that it does not simply translate existing principles of RRA into practice, but provides additional considerations not always addressed in existing RRA principles and practices thus playing a specific role in the delivery of RRA. Furthermore, the use cases show the value of SCOPE across a range of settings, including different institutional types, sizes, and missions.

Research data and software are widely accepted as an outcome of scientific work. However, in comparison to text-based publications, there is not yet an established process to assess and evaluate quality of research data and research software publications. This paper presents an attempt to fill this gap. Initiated by the Working Group Open Science of the Helmholtz Association, the Task Group Helmholtz Quality Indicators for Data and Software Publications currently develops a quality indicator for research data and research software publications to be used within the Association. This report summarizes the vision of the group of what all contributes to such an indicator. The proposed approach relies on generic well-established concepts for quality criteria, such as the FAIR Principles and the COBIT Maturity Model. It does – on purpose – not limit itself to technical implementation possibilities to avoid using an existing metric for a new purpose. The intention of this paper is to share the current state for further discussion with all stakeholders, particularly with other groups also working on similar metrics but also with entities that use the metrics.

Background There are insufficient in vitro bone models that accommodate long-term culture of osteoblasts and support their differentiation to osteocytes. The increased demand for effective therapies for bone diseases, and the ethical requirement to replace animals in research, warrants the development of such models. Here we present an in-depth protocol to prepare, create and maintain three-dimensional, in vitro, self-structuring bone models that support osteocytogenesis and long-term osteoblast survival (>1 year). Methods Osteoblastic cells are seeded on a fibrin hydrogel, cast between two beta-tricalcium phosphate anchors. Analytical methods optimised for these self-structuring bone model (SSBM) constructs, including RT-qPCR, immunofluorescence staining and XRF, are described in detail. Results Over time, the cells restructure and replace the initial matrix with a collagen-rich, mineralising one; and demonstrate differentiation towards osteocytes within 12 weeks of culture. Conclusions Whilst optimised using a secondary human cell line (hFOB 1.19), this protocol readily accommodates osteoblasts from other species (rat and mouse) and origins (primary and secondary). This simple, straightforward method creates reproducible in vitro bone models that are responsive to exogenous stimuli, offering a versatile platform for conducting preclinical translatable research studies.

Invasive plant species pose ecological threats to native ecosystems, particularly in areas adjacent to roadways, considering that roadways represent lengthy corridors through which invasive species can propagate. Traditional manual survey methods for monitoring invasive plants are labor-intensive and limited in coverage. This paper introduces a high-speed camera system, named CamAlien, designed to be mounted on vehicles for efficient invasive plant species monitoring along roadways. The camera system captures high-quality images at rapid intervals, to monitor the full roadside when following traffic speed. The system utilizes a global shutter sensor to reduce distortion and geotagging for precise localistion. The camera system makes it possible to collect extensive data sets, which can be used for a digital library of the invasive species and their locations, but also subsequent training of machine learning algorithms for automated species recognition.

Background Monte Carlo (MC) is often used when trying to assess the consequences of uncertainty in agent-based models (ABMs). However, this approach is not appropriate when the uncertainty is epistemic rather than aleatory, that is, when it represents a lack of knowledge rather than variation. The free-for-all battleship simulation modelled here is inspired by the children’s battleship game, where each battleship is an agent. Methods The models contrast an MC implementation against an interval implementation for epistemic uncertainty. In this case, our epistemic uncertainty is in the form of an imperfect radar. In the interval method, the approach occludes the status of the agents (ships) and precludes an analyst from making decisions about them in real-time. Results In a highly uncertain environment, after many time steps, there can be many ships remaining whose status is unknown. In contrast, any MC simulation invariably tends to conclude with a small number of the remaining ships after many time steps. Thus, the interval approach misses the quantitative conclusion. However, some quantitative results are generated by the interval implementation, e.g. the identities of the surviving ships, which are revealed to be nearly mutual with the MC implementation, though with fewer identities in total compared to MC. Conclusions We have demonstrated that it is possible to implement intervals in an ABM, but the results are broad, which may be useful for generating the overall bounds of the system but do not provide insight on the expected outcomes and trends.

With diminishing returns and high clinical failure rates from traditional preclinical and animal-based drug discovery strategies, more emphasis is being placed on alternative drug discovery platforms. Ex vivo approaches represent a departure from both more traditional preclinical animal-based models and clinical-based strategies and aim to address intra-tumoural and inter-patient variability at an earlier stage of drug discovery. Additionally, these approaches could also offer precise treatment stratification for patients within a week of tumour resection in order to direct tailored therapy. One tumour group that could significantly benefit from such ex vivo approaches are high-grade gliomas, which exhibit extensive heterogeneity, cellular plasticity and therapy-resistant glioma stem cell (GSC) niches. Historic use of murine-based preclinical models for these tumours has largely failed to generate new therapies, resulting in relatively stagnant and unacceptable survival rates of around 12-15 months post-diagnosis over the last 50 years. The near universal use of DNA damaging chemoradiotherapy after surgical resection within standard-of-care (SoC) therapy regimens provides an opportunity to improve current treatments if we can identify efficient drug combinations in preclinical models that better reflect the complex inter-/intra-tumour heterogeneity, GSC plasticity and inherent DNA damage resistance mechanisms. We have therefore developed and optimised a high-throughput ex vivo drug screening platform; GliExP, which maintains GSC populations using immediately dissociated fresh surgical tissue. As a proof-of-concept for GliExP, we have optimised SoC therapy responses and screened 30+ small molecule therapeutics and preclinical compounds against tumours from 18 different patients, including multi-region spatial heterogeneity sampling from several individual tumours. Our data therefore provides a strong basis to build upon GliExP to incorporate combination-based oncology therapeutics in tandem with SoC therapies as an important preclinical alternative to murine models (reduction and replacement) to triage experimental therapeutics for clinical translation and deliver rapid identification of effective treatment strategies for individual gliomas.

Multi-view datasets are becoming increasingly prevalent. These datasets consist of different modalities that provide complementary characterizations of the same underlying system. They can include heterogeneous types of information with complex relationships, varying degrees of missingness, and assorted sample sizes, as is often the case in multi-omic biological studies. Clustering multi-view data allows us to leverage different modalities to infer underlying systematic structure, but most existing approaches are limited to contexts in which entities are the same across views or have clear one-to-one relationships across data types with a common sample size. Many methods also make strong assumptions about the similarities of clusterings across views. We propose a Bayesian multi-view clustering approach (BMVC) which can handle the realities of multi-view datasets that often have complex relationships and diverse structure. BMVC incorporates known and complex many-to-many relationships between entities via a probabilistic graphical model that enables the joint inference of clusterings specific to each view, but where each view informs the others. Additionally, BMVC estimates the strength of the relationships between each pair of views, thus moderating the degree to which it imposes dependence constraints. We benchmarked BMVC on simulated data to show that it accurately estimates varying degrees of inter-view dependence when inter-view relationships are not limited to one-to-one correspondence. Next, we demonstrated its ability to capture visually interpretable inter-view structure in a public health survey of individuals and households in Puerto Rico following Hurricane Maria. Finally, we showed that BMVC clusters integrate the complex relationships between multi-omic profiles of breast cancer patient data, improving the biological homogeneity of clusters and elucidating hypotheses for functional biological mechanisms. We found that BMVC leverages complex inter-view structure to produce higher quality clusters than those generated by standard approaches. We also showed that BMVC is a valuable tool for real-world discovery and hypothesis generation.

Twenty20 cricket and batting in particular have remained vastly understudied to date. To elucidate the effects of batting on the batter, tools which replicate match play in controlled environments are essential. This study describes the development of two Twenty20 batting simulations, for a high and low strike rate innings, generated from retrospective analysis of international and domestic competition. Per delivery analysis of probabilities of run-type and on/off-strike denomination produce ball-by-ball simulations most congruent with retrospective competitive innings. Furthermore, both simulations are matched for duration and dictated through audio files. The `high' strike rate innings requires a batter to score 88 runs from 51 deliveries, whereby 60 runs are from boundaries. Similarly, the `low' strike rate innings requires a batter to score 61 runs from 51 deliveries, where 27 runs are scored from boundaries. Because batting simulations dictate run scoring outcomes, a method of quantifying a batter's performance from bat-ball contact scores is described. Ten elite batters achieved a mean performance score of 72 (SD = 26) and 88 (21) for the low and high strike rate simulations respectively. This study provides sport practitioners with a training technique to improve specific skill acquisition and enables research in understudied Twenty20 batting.

Animal models have proven integral to broadening our understanding of complex cardiac diseases but have been hampered by significant species-dependent differences in cellular physiology. Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have shown great promise in the modelling of cardiac diseases despite limitations in functional and structural maturity. 3D stem cell-derived cardiac models represent a step towards mimicking the intricate microenvironment present in the heart as an in vitro model. Incorporation of non-myocyte cell types, such as cardiac fibroblasts, into engineered heart tissue models (EHTs) can help better recapitulate the cell-to-cell and cell-to-matrix interactions present in the human myocardium. Integration of human-induced pluripotent stem cell-derived cardiac fibroblasts (hiPSC-CFs) and hiPSC-CM into EHT models enables the generation of a genetically homogeneous modelling system capable of exploring the abstruse structural and electrophysiological interplay present in cardiac pathophysiology. Furthermore, the construction of more physiologically relevant 3D cardiac models offers great potential in the replacement of animals in heart disease research. Here we describe efficient and reproducible protocols for the differentiation of hiPSC-CMs and hiPSC-CFs and their subsequent assimilation into EHTs. The resultant EHT consists of longitudinally arranged iPSC-CMs, incorporated alongside hiPSC-CFs. EHTs with both hiPSC-CMs and hiPSC-CFs exhibit slower beating frequencies and enhanced contractile force compared to those composed of hiPSC-CMs alone. The modified protocol may help better characterise the interplay between different cell types in the myocardium and their contribution to structural remodelling and cardiac fibrosis.

Background Human activity recognition poses a complex challenge in predicting individuals’ movements from raw sensor data using machine learning models. This paper explores the application of six prominent machine learning techniques – decision tree, random forest, linear regression, Naïve Bayes, k-nearest neighbor, and neural networks – to enhance the accuracy of human activity detection for e-health systems. Despite previous research efforts employing data mining and machine learning, there remains room for improvement in performance. The study focuses on predicting activities such as walking, standing, laying, sitting, walking upstairs, and walking downstairs. Methods The research employs six machine learning algorithms to recognize human activities, including decision tree, random forest, linear regression, Naïve Bayes, k-nearest neighbor, and neural networks. Results Evaluation of the human activity recognition dataset reveals that the random forest classifier, CNN, GRN and neural network yield promising results, achieving high accuracy. However, Naïve Bayes falls short of satisfying outcomes. Conclusions The study successfully classifies activities like SITTING, STANDING, LAYING, WALKING, WALKING_DOWNSTAIRS, and WALKING_UPSTAIRS with a remarkable accuracy of 98%. The contribution lies in the thorough exploration of machine learning techniques, with neural networks emerging as the most effective in enhancing human activity recognition. The findings showcase the potential for advanced applications in e-health systems and beyond.

The Internet of Things (IoT) is leading the physical and digital world of technology to converge. Real-time and massive scale connections produce a large amount of versatile data, where Big Data comes into the picture. Big Data refers to large, diverse sets of information with dimensions that go beyond the capabilities of widely used database management systems, or standard data processing software tools to manage within a given limit. Almost every big dataset is dirty and may contain missing data, mistyping, inaccuracies, and many more issues that impact Big Data analytics performances. One of the biggest challenges in Big Data analytics is to discover and repair dirty data; failure to do this can lead to inaccurate analytics results and unpredictable conclusions. Different imputation methods were employed in the experimentation with various missing value imputation techniques, and the performances of machine learning (ML) models were compared. A hybrid model that integrates ML and sample-based statistical techniques for missing value imputation is being proposed. Furthermore, the continuation involved the dataset with the best missing value imputation, chosen based on ML model performance for subsequent feature engineering and hyperparameter tuning. K-means clustering and principal component analysis were applied in our study. Accuracy, the evaluated outcome, improved dramatically and proved that the XGBoost model gives very high accuracy at around 0.125 root mean squared logarithmic error (RMSLE). To overcome overfitting, K-fold cross-validation was implemented.

Metastasis occurs when cancer cells leave the primary tumour and travel to a secondary site to form a new lesion. The tumour microenvironment (TME) is recognised to greatly influence this process, with for instance the vascular system enabling the dissemination of the cells into other tissues. However, understanding the exact role of these microenvironmental cells during metastasis has proven challenging. Indeed, in vitro models often appear too simplistic, and the study of the interactions between different cell types in a 3D space is limited. On the other hand, even though in vivo models incorporate the TME, observing cells in real-time to understand their exact role is difficult. Horizontal compartmentalised microfluidic models are a promising new platform for metastasis studies. These devices, composed of adjacent microchannels, can incorporate multiple cell types within a 3D space. Furthermore, the transparency and thickness of these models also enables high quality real-time imaging to be performed. This paper demonstrates how these devices can be successfully used for oral squamous cell carcinoma (OSCC) metastasis studies, focusing on the role of the vascular system in this process. Conditions for co-culture of OSCC cells and endothelial cells have been determined and staining protocols optimised. Furthermore, several imaging analysis techniques for these models are described, enabling precise segmentation of the different cell types on the images as well as accurate assessment of their phenotype. These methods can be applied to any study aiming to understand the role of microenvironmental cell types in cancer metastatic dissemination, and overcome several challenges encountered with current in vitro and in vivo models. Hence, this new in vitro model capable of recapitulating important aspects of the cellular complexity of human metastatic dissemination can ultimately contribute to replacing animal studies in this field.

Background: Since its first description by Babcock in the 1930’s, the pull-through delayed coloanal anastomosis (DCA) has lost interest in favor of the immediate coloanal anastomosis (ICA) with temporary stoma; especially with the advent of surgical stapling devices. In the last few years, there has been a renewed focus on DCA due to published favorable results, notably the reduced rate of anastomotic fistulas (AF) without the need for a stoma. Some authors have brought some modifications to the original technique. In this article, we describe the modified delayed coloanal anastomosis (mDCA) as it is performed in the Department of Oncologic Surgery at the Debussy Clinic. Methods: The mDCA consists on the one hand in shortening the pulled-through colonic stump, and on the other hand in performing a high anastomosis at the upper part of the anal canal. Results: This technique was successfully carried out in three patients with low rectal cancer. No AF was observed and diversion ileostomy was avoided in all patients. Conclusions: The mDCA is a feasible and safe approach for the treatment of rectal cancer by minimal invasive surgery.

The Stimulating Peripheral Activity to Relieve Conditions (SPARC) data portal is a platform that allows users to access large sets of curated data and tools. Data on the portal is sourced from researchers working in a range of disciplines applied to peripheral nerve activity, including imaging, computational biology, electrophysiology and clinical research. However, many existing tutorials and examples for SPARC do not document examples of interfaces between tools within the SPARC ecosystem. Thus, we see an opportunity to lower the barrier for entry into the SPARC ecosystem, by creating accessible guides to create workflows using multiple SPARC tools. To do this, we have developed an online hub for hosting tutorials that can guide new users through the entire workflow of acquiring data, processing it, and visualizing it, using common, open-source scientific computing tools and crucially, spanning the repertoire of currently available SPARC tools. Our approach encapsulates the workflow within a Jupyter Notebook, which is designed to be readily accessible to new users of SPARC. Having a hub of tutorials that can be easily understood will not only accelerate the growth of the SPARC community but also increase the uptake and usage of tools by members of the community.

Background Novel coronavirus disease has been recently a concern for worldwide public health. To determine epidemic rate probability at any time in any region of interest, one needs efficient bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the multi-dimensionality advantage, that suggested methodology offers, namely dealing efficiently with multiple regions at the same time and accounting for cross-correlations between different regional observations. Methods Modern multi-dimensional novel statistical method was directly applied to raw clinical data, able to deal with territorial mapping. Novel reliability method based on statistical extreme value theory has been suggested to deal with challenging epidemic forecast. Authors used MATLAB optimization software. Results This paper described a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. Namely, accurate maximum recorded patient numbers are predicted for the years to come for the analyzed provinces. Conclusions The suggested method performed well by supplying not only an estimate but 95% confidence interval as well. Note that suggested methodology is not limited to any specific epidemics or any specific terrain, namely its truly general. The only assumption and limitation is bio-system stationarity, alternatively trend analysis should be performed first. The suggested methodology can be used in various public health applications, based on their clinical survey data.

Background Therapeutically applied Minecraft groups are an intervention designed to support social engagement, confidence, and competence in youth. The flexible interaction format and use of a popular digital game support the fit of this intervention for use with neurodivergent youth. Minecraft is leveraged to support opportunities to build authentic relationships and social confidence in an engaging, low-stakes environment with peers. The group format allows for real-world social practice with peers, while the game environment can support motivation to interact with others, and provides multiple modes for such interaction (i.e., chat, building/movement with the avatar). Methods This article outlines the theoretical foundations of therapeutically applied Minecraft groups as well as practical considerations for implementation. The method outlined includes the justification for this method, process of creating support groups, check-in and check-out processes, and in-game activity examples for different situations. Use Cases Use cases are included to illustrate how the methods have been used in the past to support social growth with neurodivergent youth. Use cases include examples of different Minecraft servers, such as the habitat, and identifying stresses of social growth such as school anxiety and how the use of therapeutically applied Minecraft helped. Conclusions Therapeutically applied Minecraft groups can provide opportunities for peer connection and social practice in a facilitated environment. Though the use of Minecraft and other games to support learning and social connection is prevalent in the media, the academic research in this area is sparse. This article provides an example of Minecraft in use in a therapeutic context and calls for more formal research in this area of therapeutically applied games.

Background: Sexual self-concept has a central role in the life of the elderly population. Indeed, their sexual self-concept has significant and positive relationships with their satisfaction with life, pleasure, and willingness to interact with others. However, social-cultural prejudice means that the elderly are considered asexual individuals, harming their sexual self-concept. This prejudice is prominent in Chile, where the elderly do not have access to clear information about their sexuality. However, research on the Chilean elderly population is still in its infancy and requires more attention. Hence, this research aims to construct and validate a scale of sexual self-concept for the elderly Chilean population to cover this identified gap in the literature. Methods: Sixty items were integrated into the first version of the scale. Ten external judges were asked to assess the content validity. Twenty-eight items were maintained. Subsequently, an instrumental and cross-sectional design was implemented with a non-probabilistic sampling (N = 188). Items were refined with corrected homogeneity indices and conditional estimates of Cronbach’s alpha and Omega coefficient. Results: A final scale of nine items equally distributed in three dimensions was obtained: Sexual self-efficacy (ω = 0.867), Sexual assertiveness (ω = 0.764), and Sexual self-esteem (ω = 0.803). The confirmatory factor analysis reflects that the theoretical model has an adequate fit (CFI = .989; TLI = .984; RMSEA = .086). Conclusions: The data analyses confirmed that the scale has adequate psychometric properties. This scale can be used for multidimensional measurements of sexual self-concept in the elderly in Chile. Further research can confirm its psychometric properties in different settings within the Spanish language population.

Background Single-cell RNA sequencing (scRNA-seq) technologies have rapidly developed in recent years. The droplet-based single cell platforms enable the profiling of gene expression in tens of thousands of cells per sample. The goal of a typical scRNA-seq analysis is to identify different cell subpopulations and their respective marker genes. Additionally, trajectory analysis can be used to infer the developmental or differentiation trajectories of cells. Methods This article demonstrates a comprehensive workflow for performing trajectory inference and time course analysis on a multi-sample single-cell RNA-seq experiment of the mouse mammary gland. The workflow uses open-source R software packages and covers all steps of the analysis pipeline, including quality control, doublet prediction, normalization, integration, dimension reduction, cell clustering, trajectory inference, and pseudo-bulk time course analysis. Sample integration and cell clustering follows the Seurat pipeline while the trajectory inference is conducted using the monocle3 package. The pseudo-bulk time course analysis uses the quasi-likelihood framework of edgeR. Results Cells are ordered and positioned along a pseudotime trajectory that represented a biological process of cell differentiation and development. The study successfully identified genes that were significantly associated with pseudotime in the mouse mammary gland. Conclusions The demonstrated workflow provides a valuable resource for researchers conducting scRNA-seq analysis using open-source software packages. The study successfully demonstrated the usefulness of trajectory analysis for understanding the developmental or differentiation trajectories of cells. This analysis can be applied to various biological processes such as cell development or disease progression, and can help identify potential biomarkers or therapeutic targets.

Background The CONSORT guidelines for clinical trials recommend use of a single primary outcome, to guard against the raised risk of false positive findings when multiple measures are considered. It is, however, possible to include a suite of multiple outcomes in an intervention study, while controlling the familywise error rate, if the criterion for rejecting the null hypothesis specifies that N or more of the outcomes reach an agreed level of statistical significance, where N depends on the total number of outcome measures included in the study, and the correlation between them. Methods Simulations were run, using a conventional null-hypothesis significance testing approach with alpha set at .05, to explore the case when between 2 and 12 outcome measures are included to compare two groups, with average correlation between measures ranging from zero to .8, and true effect size ranging from 0 to .7. In step 1, a table is created giving the minimum N significant outcomes (MinNSig) that is required for a given set of outcome measures to control the familywise error rate at 5%. In step 2, data are simulated using MinNSig values for each set of correlated outcomes and the resulting proportion of significant results is computed for different sample sizes,correlations, and effect sizes. Results The Adjust NVar approach can achieve a more efficient trade-off between power and type I error rate than use of a single outcome when there are three or more moderately intercorrelated outcome variables. Conclusions Where it is feasible to have a suite of moderately correlated outcome measures, then this might be a more efficient approach than reliance on a single primary outcome measure in an intervention study. In effect, it builds in an internal replication to the study. This approach can also be used to evaluate published intervention studies.

Background: Ensuring the validity of results from funded programs is a critical concern for agencies that sponsor biological research. In recent years, the open science movement has sought to promote reproducibility by encouraging sharing not only of finished manuscripts but also of data and code supporting their findings. While these innovations have lent support to third-party efforts to replicate calculations underlying key results in the scientific literature, fields of inquiry where privacy considerations or other sensitivities preclude the broad distribution of raw data or analysis may require a more targeted approach to promote the quality of research output. Methods: We describe efforts oriented toward this goal that were implemented in one human performance research program, Measuring Biological Aptitude, organized by the Defense Advanced Research Project Agency's Biological Technologies Office. Our team implemented a four-pronged independent verification and validation (IV&V) strategy including 1) a centralized data storage and exchange platform, 2) quality assurance and quality control (QA/QC) of data collection, 3) test and evaluation of performer models, and 4) an archival software and data repository. Results: Our IV&V plan was carried out with assistance from both the funding agency and participating teams of researchers. QA/QC of data acquisition aided in process improvement and the flagging of experimental errors. Holdout validation set tests provided an independent gauge of model performance. Conclusions: In circumstances that do not support a fully open approach to scientific criticism, standing up independent teams to cross-check and validate the results generated by primary investigators can be an important tool to promote reproducibility of results.

Background: The question whether a given set of test items can be considered “identical” is often addressed in terms of the homogeneity of the test material from which said items were taken. However, for some types of matrices – in particular, for matrices consisting of minute separate particles, only some of which carry the analyte under consideration – even in the case of homogenous test material, an irreducible source of variability between test items may remain: the fundamental variability. Methods: In this paper, the concept of fundamental variability is explained, and procedures for reducing and characterizing it are described. Results: The proposed procedure allows both the detection and quantification of the fundamental variability. In addition, it is shown how fundamental variability cannot be identified in homogeneity testing according to Annex B.1.1 in ISO 13528. Conclusions: The fundamental variability can be brought under control by increasing sample size and by finer grinding of samples. Both of these measures are tantamount to increasing the average number of carrier particles per subsample. The procedure proposed in this paper for characterizing the fundamental variability requires a high number of individual analyses. It therefore makes practical sense to link the proposed procedure with suitable model assumptions in order to then arrive at an estimate of fundamental variability in routine applications with much less effort. The case of heterogenous samples lies beyond the scope of ISO 5725. The question whether irreducible sample heterogeneity should be considered an actual component of an analytical method’s precision has perhaps not yet received sufficient attention. One possible approach would be to distinguish method precision per se (laboratory bias and repeatability precision) from matrix heterogeneity, and to provide information regarding both. Certainly, if the fundamental variability is non-negligible, then it must be accounted for in any evaluation of measurement uncertainty.

Annotating the genomes of multiple species allows us to analyze the evolution of their genes. While many eukaryotic genome assemblies already include computational gene predictions, these predictions can benefit from review and refinement through manual gene annotation. The Genomics Education Partnership (GEP; https://thegep.org/) developed a structural annotation protocol for protein-coding genes that enables undergraduate student and faculty researchers to create high-quality gene annotations that can be utilized in subsequent scientific investigations. For example, this protocol has been utilized by the GEP faculty to engage undergraduate students in the comparative annotation of genes involved in the insulin signaling pathway in 27 Drosophila species, using D. melanogaster as the reference genome. Students construct gene models using multiple lines of computational and empirical evidence including expression data (e.g., RNA-Seq), sequence similarity (e.g., BLAST and multiple sequence alignment), and computational gene predictions. Quality control measures require each gene be annotated by at least two students working independently, followed by reconciliation of the submitted gene models by a more experienced student. This article provides an overview of the annotation protocol and describes how discrepancies in student submitted gene models are resolved to produce a final, high-quality gene set suitable for subsequent analyses. The protocol can be adapted to other scientific questions (e.g., expansion of the Drosophila Muller F element) and species (e.g., parasitoid wasps) to provide additional opportunities for undergraduate students to participate in genomics research. These student annotation efforts can substantially improve the quality of gene annotations in publicly available genomic databases.

Scientists write research articles, process ethics reviews, evaluate proposals and research, and seek funding. Several strategies have been proposed to optimize these operations and to decentralize access to research resources and opportunities. For instance, we previously proposed the trinity review method, combining registered reports with financing and research ethics assessments. However, previously proposed systems have a number of shortcomings, including how to implement them, e.g., who manages them, how incentives for reviewers are paid, etc. Various solutions have been proposed to address these issues, employing methods based on blockchain technologies, called “decentralized science (DeSci)”. Decentralized approaches that exploit these developments offer potentially profound improvements to the troubled scientific ecosystem. Here, we propose a system that integrates ethics reviews, peer reviews, and funding in a decentralized manner, based on Web3 technology. This new method, named ABCDEF publishing, would enhance the speed, fairness, and transparency of scientific research and publishing.

Background: Misophonia is a recently identified condition in which a person perceives a subtle stimulus (e.g., eating sounds, hair twirling) and has an intense, negative emotional response. Misophonia cannot be classified with established nosological systems. Methods: We present a novel five-phase model of misophonia from a cognitive-behavioral framework. This model identifies a learned reflex of the autonomic nervous system as the primary etiology and maintenance of misophonia. Phase one is anticipatory anxiety and avoidance. Phase two is a conditioned physical reflex (for example, the tensing of calf muscles) that develops through stimulus-response Pavlovian conditioning. Phase three includes intense negative emotional responses and accompanying physiological distress, thoughts, urges, and emotion-driven behavior. Phase four is the individual’s coping responses to emotional distress, and phase five is the environmental response and resulting internal and external consequences of the coping behaviors. Each phase helps explain the maintenance of the response and the individual’s impairment. Results: Anticipatory anxiety and avoidance of phase one contributes to an increased arousal and awareness of triggers, resulting in increased severity of the trigger experience. Both the Pavlovian-conditioned physical reflex of phase two and the emotion-driven behavior caused by the conditioned emotional response of phase three increase with in vivo exposure to triggers. Phase four includes internal and external coping behaviors to the intense emotions and distress, and phase five includes the consequences of those behaviors. Internal consequences include beliefs fiveand new emotions based on environmental responses to anger and panic. For example, the development of emotions such as shame and guilt, and beliefs regarding how ‘intolerable’ the trigger is. Conclusions: We assert misophonia is a multi-sensory condition and includes anticipatory anxiety, conditioned physical reflexes, intense emotional and physical distress, subsequent internal and external responses, and environmental consequences.

Background: Reelin has fundamental functions in the developing and mature brain. Its absence gives rise to the Reeler phenotype in mice, the first described cerebellar mutation. In homozygous mutants missing the Reelin gene ( reln -/-), neurons are incapable of correctly positioning themselves in layered brain areas such as the cerebral and cerebellar cortices. We here demonstrate that by employing ex vivo cultured cerebellar slices one can reduce the number of animals and use a non-recovery procedure to analyze the effects of Reelin on the migration of Purkinje neurons (PNs). Methods: We generated mouse hybrids (L7-GFP relnF1/) with green fluorescent protein (GFP)-tagged PNs, directly visible under fluorescence microscopy. We then cultured the slices obtained from mice with different reln genotypes and demonstrated that when the slices from reln -/- mutants were co-cultured with those from reln +/- mice, the Reelin produced by the latter induced migration of the PNs to partially rescue the normal layered cortical histology. We have confirmed this observation with Voronoi tessellation to analyze PN dispersion. Results: In images of the co-cultured slices from reln -/- mice, Voronoi polygons were larger than in single-cultured slices of the same genetic background but smaller than those generated from slices of reln +/- animals. The mean roundness factor, area disorder, and roundness factor homogeneity were different when slices from reln -/- mice were cultivated singularly or co-cultivated, supporting mathematically the transition from the clustered organization of the PNs in the absence of Reelin to a layered structure when the protein is supplied ex vivo. Conclusions: Neurobiologists are the primary target users of this 3Rs approach. They should adopt it for the possibility to study and manipulate ex vivo the activity of a brain-secreted or genetically engineered protein (scientific perspective), the potential reduction (up to 20%) of the animals used, and the total avoidance of severe surgery (3Rs perspective).

Background: The recent outbreaks of novel endemic and pandemic diseases have highlighted the importance of collaborative networks in rapid response to emerging pathogens. Over the last two decades International AIDS Vaccine Initiative (IAVI), with the support of United States Agency for International Development (USAID) and other international donors, has invested in research capacity and infrastructure in Africa. A significant portion of this support has facilitated establishing regional centers of excellence for African scientists to develop and lead a collaborative research agenda, implemented within the IAVI-led Accelerate the Development of Vaccines and New Technologies to Combat the AIDS Epidemic (ADVANCE) program. One such regional center is the University of Nairobi’s Kenya AIDS Vaccine Initiative-Institute of Clinical Research (KAVI-ICR). Objective: We designed and implemented a development program to foster inter-institutional South-South technology transfer within Africa, and address a capacity gap in mucosal research. Methods: KAVI-ICR and IAVI developed standardized mucosal sample collection, processing and technical assay methods; these were subsequently applied into several observational studies, and Phase I HIV vaccines, Varicella zoster virus vaccine, and broadly neutralizing antibodies clinical trials at KAVI-ICR. Thereafter, KAVI-ICR facilitated the technology transfer of the methods, by training staff at regional establishments in Africa. Results: Twelve standardized methodologies were developed for the collection, processing and storage of 10 mucosal sample types. Subsequently, eight regional research centers received training for a variety of clinical and laboratory methodologies; the centers later applied the techniques in follow-up collaborative research. Additionally, the training fostered collaboration while allowing the development of local networks of research groups. Conclusion: By such South-South initiatives, supported by international donors, the development of regional capacity and expertise is realizable. The established expertise can be leveraged when needed, and builds the capability for African scientists to engage at an international level, actively participating in driving internationally relevant research.

Background: Research and researchers are heavily evaluated, and over the past decade it has become apparent that the consequences of evaluating the research enterprise and particularly individual researchers are considerable. This has resulted in the publishing of several guidelines and principles to support moving towards more responsible research assessment (RRA). To ensure that research evaluation is meaningful, responsible, and effective the International Network of Research Management Societies (INORMS) Research Evaluation Group created the SCOPE framework enabling evaluators to deliver on existing principles of RRA. SCOPE bridges the gap between principles and their implementation by providing a structured five-stage framework by which evaluations can be designed and implemented, as well as evaluated. Methods: SCOPE is a step-by-step process designed to help plan, design, and conduct research evaluations as well as check effectiveness of existing evaluations. In this article, four case studies are presented to show how SCOPE has been used in practice to provide value-based research evaluation. Results: This article situates SCOPE within the international work towards more meaningful and robust research evaluation practices and shows through the four case studies how it can be used by different organisations to develop evaluations at different levels of granularity and in different settings. Conclusions: The article demonstrates that the SCOPE framework is rooted firmly in the existing literature. In addition, it is argued that it does not simply translate existing principles of RRA into practice, but provides additional considerations not always addressed in existing RRA principles and practices thus playing a specific role in the delivery of RRA. Furthermore, the use cases show the value of SCOPE across a range of settings, including different institutional types, sizes, and missions.

Animal models have proven integral to broadening our understanding of complex cardiac diseases but have been hampered by significant species-dependent differences in cellular physiology. Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have shown great promise in the modelling of cardiac diseases despite limitations in functional and structural maturity. 3D stem cell-derived cardiac models represent a step towards mimicking the intricate microenvironment present in the heart as an in vitro model. Incorporation of non-myocyte cell types, such as cardiac fibroblasts, into engineered heart tissue models (EHTs) can help better recapitulate the cell-to-cell and cell-to-matrix interactions present in the human myocardium. Integration of human-induced pluripotent stem cell-derived cardiac fibroblasts (hiPSC-CFs) and hiPSC-CM into EHT models enables the generation of a genetically homogeneous modelling system capable of exploring the abstruse structural and electrophysiological interplay present in cardiac pathophysiology. Furthermore, the construction of more physiologically relevant 3D cardiac models offers great potential in the replacement of animals in heart disease research. Here we describe efficient and reproducible protocols for the differentiation of hiPSC-CMs and hiPSC-CFs and their subsequent assimilation into EHTs. The resultant EHT consists of longitudinally arranged iPSC-CMs, incorporated alongside hiPSC-CFs. EHTs with both hiPSC-CMs and hiPSC-CFs exhibit slower beating frequencies and enhanced contractile force compared to those composed of hiPSC-CMs alone. The modified protocol may help better characterise the interplay between different cell types in the myocardium and their contribution to structural remodelling and cardiac fibrosis.

Background: Monte Carlo (MC) is often used when trying to assess the consequences of uncertainty in agent-based models (ABMs). However, this approach is not appropriate when the uncertainty is epistemic rather than aleatory, that is, when it represents a lack of knowledge rather than variation. The free-for-all battleship simulation modelled here is inspired by the children’s battleship game, where each battleship is an agent. Methods: The models contrast an MC implementation against an interval implementation for epistemic uncertainty. In this case, our epistemic uncertainty is in the form of an uncertain radar. In the interval method, the approach occludes the status of the agents (ships) and precludes an analyst from making decisions about them in real-time. Results: In a highly uncertain environment, after many time steps, there can be many ships remaining whose status is unknown. In contrast, any MC simulation invariably tends to conclude with a small number of the remaining ships after many time steps. Thus, the interval approach misses the quantitative conclusion. However, some quantitative results are generated by the interval implementation, e.g. the identities of the surviving ships, which are revealed to be nearly mutual with the MC implementation, though with fewer identities in total compared to MC. Conclusions: We have demonstrated that it is possible to implement intervals in an ABM, but the results are broad, which may be useful for generating the overall bounds of the system but do not provide insight on the expected outcomes and trends.

With diminishing returns and high clinical failure rates from traditional preclinical and animal-based drug discovery strategies, more emphasis is being placed on alternative drug discovery platforms. Ex vivo approaches represent a departure from both more traditional preclinical animal-based models and clinical-based strategies and aim to address intra-tumoural and inter-patient variability at an earlier stage of drug discovery. Additionally, these approaches could also offer precise treatment stratification for patients within a week of tumour resection in order to direct tailored therapy. One tumour group that could significantly benefit from such ex vivo approaches are high-grade gliomas, which exhibit extensive heterogeneity, cellular plasticity and therapy-resistant glioma stem cell (GSC) niches. Historic use of murine-based preclinical models for these tumours has largely failed to generate new therapies, resulting in relatively stagnant and unacceptable survival rates of around 12-15 months post-diagnosis over the last 50 years. The near universal use of DNA damaging chemoradiotherapy after surgical resection within standard-of-care (SoC) therapy regimens provides an opportunity to improve current treatments if we can identify efficient drug combinations in preclinical models that better reflect the complex inter-/intra-tumour heterogeneity, GSC plasticity and inherent DNA damage resistance mechanisms. We have therefore developed and optimised a high-throughput ex vivo drug screening platform; GliExP, which maintains GSC populations using immediately dissociated fresh surgical tissue. As a proof-of-concept for GliExP, we have optimised SoC therapy responses and screened 30+ small molecule therapeutics and preclinical compounds against tumours from 18 different patients, including multi-region spatial heterogeneity sampling from several individual tumours. Our data therefore provides a strong basis to build upon GliExP to incorporate combination-based oncology therapeutics in tandem with SoC therapies as an important preclinical alternative to murine models (reduction and replacement) to triage experimental therapeutics for clinical translation and deliver rapid identification of effective treatment strategies for individual gliomas.

Background: Misophonia is a recently identified condition in which a person perceives a subtle stimulus (e.g., eating sounds, hair twirling) and has an intense, negative emotional response. Misophonia cannot be classified with established nosological systems. Methods: We present a novel five-phase model of misophonia from a cognitive-behavioral framework. This model identifies a learned reflex of the autonomic nervous system as the primary etiology and maintenance of misophonia. Phase one is anticipatory anxiety and avoidance. Phase two is a conditioned physical reflex (for example, the tensing of calf muscles) that develops through stimulus-response Pavlovian conditioning. Phase three includes intense negative emotional responses and accompanying physiological distress, thoughts, urges, and emotion-driven behavior. Phase four is the individual’s coping responses to emotional distress, and phase five is the environmental response and resulting internal and external consequences of the coping behaviors. Each phase helps explain the maintenance of the response and the individual’s impairment. Results: Anticipatory anxiety and avoidance of phase one contributes to an increased arousal and awareness of triggers, resulting in increased severity of the trigger experience. Both the Pavlovian-conditioned physical reflex of phase two and the emotion-driven behavior caused by the conditioned emotional response of phase three increase with in vivo exposure to triggers. Phase four includes internal and external coping behaviors to the intense emotions and distress, and phase 5 includes the consequences of those behaviors. Internal consequences include beliefs and new emotions based on environmental responses to anger and panic. For example, the development of emotions such as shame and guilt, and beliefs regarding how ‘intolerable’ the trigger is. Conclusions: We assert misophonia is a multi-sensory condition and includes anticipatory anxiety, conditioned physical reflexes, intense emotional and physical distress, subsequent internal and external responses, and environmental consequences.

Annotating the genomes of multiple species allows us to analyze the evolution of their genes. While many eukaryotic genome assemblies already include computational gene predictions, these predictions can benefit from review and refinement through manual gene annotation. The Genomics Education Partnership (GEP; https://thegep.org/) developed a structural annotation protocol for protein-coding genes that enables undergraduate student and faculty researchers to create high-quality gene annotations that can be utilized in subsequent scientific investigations. For example, this protocol has been utilized by the GEP faculty to engage undergraduate students in the comparative annotation of genes involved in the insulin signaling pathway in 27 Drosophila species, using D. melanogaster as the reference genome. Students construct gene models using multiple lines of computational and empirical evidence including expression data (e.g., RNA-Seq), sequence similarity (e.g., BLAST and multiple sequence alignment), and computational gene predictions. Quality control measures require each gene be annotated by at least two students working independently, followed by reconciliation of the submitted gene models by a more experienced student. This article provides an overview of the annotation protocol and describes how discrepancies in student submitted gene models are resolved to produce a final, high-quality gene set suitable for subsequent analyses. The protocol can be adapted to other scientific questions (e.g., expansion of the Drosophila Muller F element) and species (e.g., parasitoid wasps) to provide additional opportunities for undergraduate students to participate in genomics research. These student annotation efforts can substantially improve the quality of gene annotations in publicly available genomic databases.

Background: Sleeping sickness is caused by the extracellular parasite Trypanosoma brucei and is associated with neuroinflammation and neuropsychiatric disorders, including disruption of sleep/wake patterns, and is now recognised as a circadian disorder. Sleeping sickness is traditionally studied using murine models of infection due to the lack of alternative in vitro systems that fully recapitulate the cellular diversity and functionality of the human brain. The aim of this study is to develop a much-needed in vitro system that reduces and replaces live animals for the study of infections in the central nervous system, using sleeping sickness as a model infection. Methods: We developed a co-culture system using induced pluripotent stem cell (iPSC)-derived cortical human brain organoids and the human pathogen T. b. gambiense to model host-pathogen interactions in vitro. Upon co-culture, we analysed the transcriptional responses of the brain organoids to T. b. gambiense over two time points. Results: We detected broad transcriptional changes in brain organoids exposed to T. b. gambiense, mainly associated with innate immune responses, chemotaxis, and blood vessel differentiation compared to untreated organoids. Conclusions: Our co-culture system provides novel, more ethical avenues to study host-pathogen interactions in the brain as alternative models to experimental infections in mice. Although our data support the use of brain organoids to model host-pathogen interactions during T. brucei infection as an alternative to in vivo models, future work is required to increase the complexity of the organoids ( e.g., addition of microglia and vasculature). We envision that the adoption of organoid systems is beneficial to researchers studying mechanisms of brain infection by protozoan parasites. Furthermore, organoid systems have the potential to be used to study other parasites that affect the brain significantly reducing the number of animals undergoing moderate and/or severe protocols associated with the study of neuroinflammation and brain infections.

Scientists write research articles, process ethics reviews, evaluate proposals and research, and seek funding. Several strategies have been proposed to optimize these operations and to decentralize access to research resources and opportunities. For instance, we previously proposed the trinity review method, combining registered reports with financing and research ethics assessments. However, previously proposed systems have a number of shortcomings, including how to implement them, e.g., who manages them, how incentives for reviewers are paid, etc. Various solutions have been proposed to address these issues, employing methods based on blockchain technologies, called “decentralized science (DeSci)”. Decentralized approaches that exploit these developments offer potentially profound improvements to the troubled scientific ecosystem. Here, we propose a system that integrates ethics reviews, peer reviews, and funding in a decentralized manner, based on Web3 technology. This new method, named ABCDEF publishing, would enhance the speed, fairness, and transparency of scientific research and publishing.

Background: Monte Carlo (MC) is often used when trying to assess the consequences of uncertainty in agent-based models (ABMs). However, this approach is not appropriate when the uncertainty is epistemic rather than aleatory, that is, when it represents a lack of knowledge rather than variation. The free-for-all battleship simulation modelled here is inspired by the children’s battleship game, where each battleship is an agent. Methods: The models contrast an MC implementation against an interval implementation for epistemic uncertainty. In this case, our epistemic uncertainty is in the form of an uncertain radar. In the interval method, the approach occludes the status of the agents (ships) and precludes an analyst from making decisions about them in real-time. Results: In a highly uncertain environment, after many time steps, there can be many ships remaining whose status is unknown. In contrast, any MC simulation invariably tends to conclude with a small number of the remaining ships after many time steps. Thus, the interval approach misses the quantitative conclusion. However, some quantitative results are generated by the interval implementation, e.g. the identities of the surviving ships, which are revealed to be nearly mutual with the MC implementation, though with fewer identities in total compared to MC. Conclusions: We have demonstrated that it is possible to implement intervals in an ABM, but the results are broad, which may be useful for generating the overall bounds of the system but do not provide insight on the expected outcomes and trends.

Background: Misophonia is a recently identified condition in which a person perceives a subtle stimulus (e.g., eating sounds, hair twirling) and has an intense, negative emotional response. Misophonia cannot be classified with established nosological systems. Methods: We present a novel five-phase model of misophonia from a cognitive-behavioral framework. This model identifies a learned reflex of the autonomic nervous system as the primary etiology and maintenance of misophonia. Phase one is anticipatory anxiety and avoidance. Phase two is a conditioned physical reflex (for example, the tensing of calf muscles) that develops through stimulus-response Pavlovian conditioning. Phase three includes intense negative emotional responses and accompanying physiological distress, thoughts, urges, and emotion-driven behavior. Phase four is the individual’s coping responses to emotional distress, and phase five is the environmental response and resulting internal and external consequences of the coping behaviors. Each phase helps explain the maintenance of the response and the individual’s impairment. Results: Anticipatory anxiety and avoidance of phase one contributes to an increased arousal and awareness of triggers, resulting in increased severity of the trigger experience. Both the Pavlovian-conditioned physical reflex of phase two and the emotion-driven behavior caused by the conditioned emotional response of phase three increase with in vivo exposure to triggers. Phase four includes internal and external coping behaviors to the intense emotions and distress, and phase 5 includes the consequences of those behaviors. Internal consequences include beliefs and new emotions based on environmental responses to anger and panic. For example, the development of emotions such as shame and guilt, and beliefs regarding how ‘intolerable’ the trigger is. Conclusions: We assert misophonia is a multi-sensory condition and includes anticipatory anxiety, conditioned physical reflexes, intense emotional and physical distress, subsequent internal and external responses, and environmental consequences.

Introduction: Posaconazole is a widely used antifungal drug, and its accurate quantification is essential for quality control and assessment of its pharmaceutical products. This study aimed to develop and validate a reverse-phase high-performance liquid chromatography (HPLC) analytical method for quantifying Posaconazole in bulk and dosage form. Methods: The HPLC method was developed and validated based on International Conference on Harmonisation (ICH) guidelines. The developed method was then applied to quantify Posaconazole in a marketed tablet formulation. The method's specificity, linearity, precision, accuracy, robustness, and stability were evaluated. Results: The developed HPLC method showed good linearity over a 2-20 μg/mL concentration range. The percentage recovery of Posaconazole from the bulk and marketed formulations was found to be 99.01% and 99.05%, respectively. The intra-day and inter-day precisions were less than 1%, and the method was stable under different conditions. The HPLC method was successfully applied to quantify Posaconazole in the marketed formulation. Conclusion: The developed and validated HPLC method is reliable and efficient for analyzing Posaconazole in bulk and dosage forms. The method's accuracy, precision, specificity, linearity, robustness, and stability demonstrate its effectiveness. The method can be used for the quality control and assessment of Posaconazole-containing pharmaceutical products.

We develop a thermodynamic model describing the binding of RNA binding proteins (RBP) to oligomers in vitro. We apply expectation-maximization to infer the specificity of RBPs, represented as position-specific weight matrices (PWMs), by maximizing the likelihood of RNA Bind’n Seq data from the ENCODE project. We demonstrate that the model can reproduce known specificities for well-studied proteins and that in some cases we predict novel, longer binding motifs. However, the model does not recover all the motifs that are in principle known, indicating that the data is not well explained by a single underlying biophysical model. Our code is publicly available.

Purpose: Dietary intake assessment has an essential role in chronic disease studies and general public health outcomes. To measure dietary intakes in epidemiologic studies, various dietary assessment methods are used. Among them, food frequency questionnaires (FFQs) can provide useful measures of dietary intake. This study aimed to report the development of a population-specific dish-based food frequency questionnaire (DFFQ) to assess long-term usual dietary intakes in Afghanistan. Design/Methodology/Approach: We considered the Willet format and followed these steps; listing commonly consumed Afghani foods, determining food groups, defining portion sizes, and determining the frequency response options for each food item. We prepared a comprehensive list of foods and dishes commonly used in Afghanistan. We categorized dishes and food items in the questionnaire into eight major groups. Portion sizes for food items and mixed dishes were defined based on the most commonly consumed and understandable portion sizes for each item among the Afghan general population. The frequency response options for each food item were defined separately in a row against the food list. Findings: We developed a new population-specific DFFQ in Afghanistan containing 103 food items or dishes, which can be used as an instrument for the assessment of long-term dietary intakes as well as diet-disease associations in Afghanistan. Originality/Value: This is the first ever-developed dietary assessment tool in Afghanistan.