Open Software Research Articles

Modelling count data with partial differential equation models in biology

Partial differential equation (PDE) models are often used to study biological phenomena involving movement-birth–death processes, including ecological population dynamics and the invasion of populations of biological cells. Count data, by definition, is non-negative, and count data relating to biological populations is often bounded above by some carrying capacity that arises through biological competition for space or nutrients. Parameter estimation, parameter identifiability, and making model predictions usually involves working with a measurement error model that explicitly relating experimental measurements with the solution of a mathematical model. In many biological applications, a typical approach is to assume the data are normally distributed about the solution of the mathematical model. Despite the widespread use of the standard additive Gaussian measurement error model, the assumptions inherent in this approach are rarely explicitly considered or compared with other options. Here, we interpret scratch assay data, involving migration, proliferation and delays in a population of cancer cells using a reaction–diffusion PDE model. We consider relating experimental measurements to the PDE solution using a standard additive Gaussian measurement error model alongside a comparison to a more biologically realistic binomial measurement error model. While estimates of model parameters are relatively insensitive to the choice of measurement error model, model predictions for data realisations are very sensitive. The standard additive Gaussian measurement error model leads to biologically inconsistent predictions, such as negative counts and counts that exceed the carrying capacity across a relatively large spatial region within the experiment. Furthermore, the standard additive Gaussian measurement error model requires estimating an additional parameter compared to the binomial measurement error model. In contrast, the binomial measurement error model leads to biologically plausible predictions and is simpler to implement. We provide open source Julia software on GitHub to replicate all calculations in this work, and we explain how to generalise our approach to deal with coupled PDE models with several dependent variables through a multinomial measurement error model, as well as pointing out other potential generalisations by linking our work with established practices in the field of generalised linear models.

Open package form-stable phase change microspheres with low thermal contact resistance for the thermal management of electronic devices

Thermal contact resistance (TCR) is a key factor that affects the rapid heat transfer process based on thermal interface materials (TIMs). Phase change TIMs have the property of thermally induced solid–liquid phase change, which is an advantage to improve the contact state and reduce TCR. However, the contradiction between form-stable and wettability must be solved during the application of phase change TIMs. In this work, a flexible, form-stable and low TCR phase change TIMs of graphene/acrylic polymer foamed microspheres/polyethylene glycol (G/APM/PEG) has been successfully prepared by “open package”. APM is light and the surface are covered with micropores, which can be adsorbed a large amount of liquid phase PEG to prevent leakage and retain good wettability. Furthermore, the thermal properties of G/APM/PEG were systematically studied. The TCR results between “open package” method and “closed package” method were compared. The critical thickness value in all measure conditions were calculated. The thermal conductivity of G/APM/PEG filled with graphene (4 wt%) is 230% higher than that of APM/PEG. The TCR of G/APM/PEG is 0.5～0.9 K∙cm2/W at 65 °C (40 Psi), which is far lower than that of TIMs from “closed package” (3.5～5.3 K∙cm2/W). This is because the G/APM/PEG from “open package” exposes part of the surface of PEG to air, greatly improving the contact state. In addition, the maximum critical thickness is about 0.3 mm for all G/APM/PEG under different measure conditions. It confirms that the TCR is relatively small in all measure conditions and the thermal conductivity resistance is dominant.

Complementarity of two proteomic data analysis tools in the identification of drug-metabolising enzymes and transporters in human liver.

Several software packages are available for the analysis of proteomic LC-MS/MS data, including commercial (e.g. Mascot/Progenesis LC-MS) and open access software (e.g. MaxQuant). In this study, Progenesis and MaxQuant were used to analyse the same data set from human liver microsomes (n = 23). Comparison focussed on the total number of peptides and proteins identified by the two packages. For the peptides exclusively identified by each software package, distribution of peptide length, hydrophobicity, molecular weight, isoelectric point and score were compared. Using standard cut-off peptide scores, we found an average of only 65% overlap in detected peptides, with surprisingly little consistency in the characteristics of peptides exclusively detected by each package. Generally, MaxQuant detected more peptides than Progenesis, and the additional peptides were longer and had relatively lower scores. Progenesis-specific peptides tended to be more hydrophilic and basic relative to peptides detected only by MaxQuant. At the protein level, we focussed on drug-metabolising enzymes (DMEs) and transporters, by comparing the number of unique peptides detected by the two packages for these specific proteins of interest, and their abundance. The abundance of DMEs and SLC transporters showed good correlation between the two software tools, but ABC showed less consistency. In conclusion, in order to maximise the use of MS datasets, we recommend processing with more than one software package. Together, Progenesis and MaxQuant provided excellent coverage, with a core of common peptides identified in a very robust way.

Open source as a gateway to artificial intelligence

Open source has been widely adopted across the entire solution set in the library world, including software for traditional library services, digital libraries, discovery systems, identity management and persistent identifier solutions. Open source is also foundational in the development of artificial intelligence (AI) solutions. Open source AI toolkits have driven the adoption of AI in machine learning, image recognition, video analysis and many other areas relevant to library systems. The communities supporting open source library software are among the largest and most dynamic in the library world. The Koha library management system is supported by a community of developers, librarians, service providers and participating libraries around the world, and is probably the most widely adopted library management system in the world. This article explores the evolution of open source in libraries and the ways in which the open source solutions can facilitate the introduction of AI in libraries in a way that gives libraries agency on the AI model development and implementation. Examples are provided on the use of AI toolkits in the library context.

Development of open nutritional recipe software for obese children based on intelligent computing

Development of open nutritional recipe software for obese children based on intelligent computing

Evaluation of a Virtual Reality-Based Open Educational Resource Software.

Virtual reality (VR) teaching methods have potential to support medical students acquire increasing amounts of knowledge. EVENT (Easy VR EducatioN Tool) is an open educational resource software for immersive VR environments, which is designed for use without programming skills. In this work, EVENT was used in a medical student VR course on pancreatic cancer. Medical students were invited to participate in the course. Before and after VR simulation, participants completed a multiple-choice knowledge assessment, with a maximum score of 10, and a VR experience questionnaire. The primary endpoint compared pre- and post-VR simulation test scores. Secondary endpoints included usability and factors that could affect learning growth and test results. Data from 117 of the 135 participating students was available for analysis. Student test scores improved by an average of 3.4 points (95% CI 3.1-3.7, P < 0.001) after VR course. The secondary endpoints of gender, age, prior knowledge regarding the medical subject, professional training completed in the medical field, video game play, three-dimensional imagination skills, or cyber-sickness had no major impact on test scores or final ranking (top or bottom 25%). The 27 students whose post-VR simulation test scores ranked in the top 25% had no prior experience with VR. The average System Usability Scale score was 86.1, which corresponds to an excellent outcome for user-friendliness. Questionnaire responses post-VR simulation show students (81.2% [95/117]) interest in more VR options in medical school. We present a freely available software that allows for the development of VR teaching lessons without programming skills.

Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall

AbstractA number of studies have investigated the large‐scale drivers and upstream precursors of extreme weather events, making it clear that the earliest warning signs of extreme events can be remote from the impacted region in both time and space. Integrating and leveraging our understanding of dynamical precursors provides a new perspective on ensemble forecasting for extreme events, focused on building storylines of possible event evolution. This then acts as a tool for raising awareness of the conditions conducive to high‐impact weather and providing early warning of their possible development. However, operational applications of this developing knowledge base are limited, perhaps for want of a clear framework for doing so. Here, we present such a framework, supported by open software tools, designed for identifying large‐scale precursors of categorical weather events in an automated fashion and reducing them to scalar indices suitable for statistical prediction, forecast interpretation, and model validation. We demonstrate this framework by systematically analysing the precursor circulations of daily rainfall extremes across 18 regional‐ to national‐scale European domains. We discuss the precursor rainfall dynamics for three disparate regions, and show our findings are consistent with, and extend, previous work. We provide an estimate of the predictive utility of these precursors across Europe based on logistic regression, and show that large‐scale precursors can usefully predict heavy rainfall between two and six days ahead, depending on region and season. We further show how, for more continental‐scale applications, the regionally specific precursors can be synthesised into a minimal set of indices that drive heavy precipitation. We then provide comments and guidance for generalisation and application of our demonstrated approach to new variables, timescales, and regions.

Exploring Operando Electrochemical Techniques to Track Degradation Mechanisms in Li-Ion Batteries

The battery field has recently received unprecedented attention and investments in order to handle the upcoming demands of energy storage notably from the electric mobility sector. The pursuit of higher performance batteries that store more energy, charge faster and have a longer life cycle has been converted into studies for new electrode materials, coatings, electrolytes, among others that often find the common key challenge of the performance decline. The precise identification of the degradation mechanisms and their tracking along aging is thus essential to pave the way for the next generation batteries. For that several methods are employed although most of them are not in operando, not following the practical cell operation. In that context, this paper introduces a set of operando electrochemical techniques that are still largely underexploited but that can be coupled and offer broader comprehension of the degradation mechanisms through deeper than usual analysis of electrochemical data. Taking insertion cells (like Li ion or Na ion batteries) as example it is first shown how differential techniques like incremental capacity analysis (ICA or dQ/dV vs V) and differential voltage analysis (DVA or dV/dQ vs Q) allow to identify main degradation mechanisms in the positive and in the negative electrodes separately by following their active material masses and their curves dynamics (relative displacement of the potential x capacity curves, the slippage) in a two electrodes configuration. For that the in house and free access developed software Electrochemical Visualization Application (EVA) is employed. In addition, using defined protocols, other techniques as Distribution of Relaxation Times (DRT) are coupled to those first analyses in order to complement the understanding of the degradation mechanisms in each electrode. This presentation intends to show an introduction of how to deeper analyze electrochemical techniques to follow loss of mass and lithium ion inventory from each electrode along aging, how to use the DRT with the ICA and DVA to identify the evolution of Impedance from each electrode along aging and how to build protocols that allow to couple those techniques minimizing any eventual degradation interference from those techniques, that all shown through experimental results and with the aid of a free and open software for the part of the ICA and DVA analyses. Figure 1

Presentation of the FEM analysis methodology using MathCAD software

This paper presents the authors' experience with teaching the finite element method (FEM) at a university using the MathCAD software. With the development of computational tools in the second half of the 20th century, there was also the development of computational methods aimed at algorithmizing engineering tasks based on FEM. This method is very effective for determining the forces and deformations of truss and frame structures under the influence of the external environment. Many processes in the automatic design system take place as if in a "black box", and the process of verifying the achieved results becomes the most important stage in the design activity. Without knowing the theoretical foundations of FEM, physical and mathematical modelling, verification procedures and methods, the structure design cannot be safe and reliable. In this paper, we present one of the possibilities, how a student can familiarize himself with the theoretical foundations of FEM and calculation procedures using the MathCAD software. MathCAD can be used as a vehicle to create, organize, and assemble all the pieces of the engineering calculations. MathCAD is open software for using input data from other programs.

PatentInspector: An Open-Source Tool for Applied Patent Analysis and Information Extraction

Patent analysis is a field that concerns the analysis of patent records, for the purpose of extracting insights and trends, and it is widely used in various fields. Despite the abundance of proprietary software employed for this purpose, there is currently a lack of easy-to-use and publicly available software that can offer simple and intuitive visualizations, while advocating for open science and scientific software development. In this study, we attempt to fill this gap by offering PatentInspector, an open-source, public tool that, by leveraging patent data from the United States Trademark and Patent Office, is able to produce descriptive analytics, thematic axes and citation network analysis. The use and interpretability of PatentInspector is illustrated through a use case on human resource management-related patents, highlighting its functionalities. The results indicate that PatentInspector is a practical resource for conducting patent analytics and can be used by individuals with a limited or no background in coding and software development.

Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach

BackgroundCerebral alveolar echinococcosis (CAE) and brain metastases (BM) share similar in locations and imaging appearance. However, they require distinct treatment approaches, with CAE typically treated with chemotherapy and surgery, while BM is managed with radiotherapy and targeted therapy for the primary malignancy. Accurate diagnosis is crucial due to the divergent treatment strategies.PurposeThis study aims to evaluate the effectiveness of radiomics and machine learning techniques based on magnetic resonance imaging (MRI) to differentiate between CAE and BM.MethodsWe retrospectively analyzed MRI images of 130 patients (30 CAE and 100 BM) from Xinjiang Medical University First Affiliated Hospital and The First People's Hospital of Kashi Prefecture, between January 2014 and December 2022. The dataset was divided into training (91 cases) and testing (39 cases) sets. Three dimensional tumors were segmented by radiologists from contrast-enhanced T1WI images on open resources software 3D Slicer. Features were extracted on Pyradiomics, further feature reduction was carried out using univariate analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO). Finally, we built five machine learning models, support vector machine, logistic regression, linear discrimination analysis, k-nearest neighbors classifier, and Gaussian naïve bias and evaluated their performance via several metrics including sensitivity (recall), specificity, positive predictive value (precision), negative predictive value, accuracy and the area under the curve (AUC).ResultsThe area under curve (AUC) of support vector classifier (SVC), linear discrimination analysis (LDA), k-nearest neighbors (KNN), and gaussian naïve bias (NB) algorithms in training (testing) sets are 0.99 (0.94), 1.00 (0.87), 0.98 (0.92), 0.97 (0.97), and 0.98 (0.93), respectively. Nested cross-validation demonstrated the robustness and generalizability of the models. Additionally, the calibration plot and decision curve analysis demonstrated the practical usefulness of these models in clinical practice, with lower bias toward different subgroups during decision-making.ConclusionThe combination of radiomics and machine learning approach based on contrast enhanced T1WI images could well distinguish CAE and BM. This approach holds promise in assisting doctors with accurate diagnosis and clinical decision-making.

Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data

AbstractCamera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap‐derived Big Data are becoming increasingly solvable with the help of scalable cyber‐infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media‐based and event‐based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in‐depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard.

MetalDock: An Open Access Docking Tool for Easy and Reproducible Docking of Metal Complexes.

Despite the proven potential of metal complexes as therapeutics, the lack of computational tools available for the high-throughput screening of their interactions with proteins is a limiting factor toward clinical developments. To address this challenge, we introduce MetalDock, an easy-to-use, open access docking software for docking metal complexes to proteins. Our tool integrates the AutoDock docking engine with three well-known quantum software packages to automate the docking of metal-organic complexes to proteins. We used a Monte Carlo sampling scheme to obtain the missing Lennard-Jones parameters for 12 metal atom types and demonstrated that these parameters generalize exceptionally well. Our results show that the poses obtained by MetalDock are highly accurate, as they predict the binding geometries experimentally determined by crystal structures with high spatial reproducibility. Three different case studies are presented that demonstrate the versatility of MetalDock for the docking of diverse metal-organic compounds to different biomacromolecules, including nucleic acids.

Industrial IoT system for laser-wire direct energy deposition: data collection and visualization of manufacturing process signals

Industry 4.0, also known as the Fourth Industrial Revolution, is a term used to describe the current trend of automation and data exchange in manufacturing and other industries. The Internet of Things (IoT) plays a crucial role in Industry 4.0 by connecting devices, machines, and products to the Internet and enabling real-time data exchange. Moreover, additive manufacturing is a key developing manufacturing technology in Industry 4.0. New technologies such as data analysis with Artificial Intelligence and machine vision are widely used in optimization. However, in a lab environment, these technologies depend on the data collected from the process. For such work, the researchers should be able to focus on their core research rather than on the development of infrastructure to collect and analyse the data. This research presents an open software and hardware IoT solution to monitor a laser wire direct energy deposition system installed in a cartesian type 3-axis machine tool. The IoT solution adopts three open-source tools for core issues, such as 1) interoperability, flexibility, and availability; 2) data storage; and 3) data visualization of sensor data and manufacturing process signals. The system architecture is based on one or more edge devices connected to sensors and forwarding their data toward a local API endpoint. The endpoint is created with Node-RED, an open-source visual flow-based development tool for IoT data. Node-RED forwards the data to an open-source InfluxDB database. Finally, the data is visualized with an open-source Grafana application. The system is prototyped, designed, implemented, and tested in a lab environment to monitor a laser-wire direct energy deposition process. The significance of such a flexible IoT data collection system for research and development projects can be integral. Thus, providing savings in time and money can substantially speed up the development of new technologies where the value arises from the sensor data and its analysis.

Challenges Experienced and Observed during the Implementation of Leprosy Strategies, Sidama Region, Southern Ethiopia: An inductive thematic analysis of qualitative study among health professionals who working with leprosy programs.

Prompt diagnosis and treatment of leprosy are crucial for preventing the disease's spread as well as for avoiding negative medical and social effects and reducing the disease's burden. The likelihood of nerve damage and subsequent disability rises as the length of the diagnostic delay. We aimed to explore the challenges of health professionals faced regarding their involvement in early leprosy case detection strategies. The study employed a qualitative, descriptive and phenomenological explorative research design to answer the research questions. By the use of non-probability purposive sampling, research participants were identified. During the study, in-depth interviews were conducted to gather information regarding the experiences of health workers (medical doctors, public health officers, clinical nurses, health centre heads and regional and Woreda district health office technical and programme experts) and health extension workers. To analyse the qualitative data, inductive thematic analysis techniques were used. For analysis, open code software version 4.0 was used. The data transcription, coding, display, reduction (theme) and interpretation of the discovered results were the processes undertaken for the analysis. The findings of the study revealed that leprosy prevention and control programmes are still problematic. Themes that emerged from the data gleaned from the health workers included: lack of the existence of practice-oriented training, Integration of TB and leprosy training, lack of focus or other competing health priorities, Inadequate supportive supervision of health facilities, Multiple tasks for health workers, poor coordination and communications, lack of motivation in health workers, disruption in treatment, and Importance of training related to leprosy. Strengthening comprehensive leprosy training for health workers, carrying out efficient and thorough contact tracing, enhancing monitoring, supervision, assessment and surveillance, boosting managerial skills, lobbying political commitment, and motivating healthcare workers may help in early detection of leprosy cases strategies.

Routine Whole Genome Sequencing Offers a More Comprehensive Assessment of IG/TCR Gene Rearrangements for MRD Targets in Acute Lymphoblastic Leukemia - Data from the First 62 Cases from the NHS England Genomic Medicine Service

Introduction Minimal residual disease (MRD) monitoring is fundamental for risk stratification and treatment in acute lymphoblastic leukemia (ALL). Sensitive molecular assay requires presentation samples to be screened for immunoglobulin and T-cell receptor (IG/TCR) gene rearrangements. Previously samples were screened by PCR, heteroduplex analysis and Sanger sequencing to identify clone defining IG/TCR gene rearrangements. Subsequently these sequences were used to develop qPCR assays for MRD follow-up monitoring. More recently, targeted IG/TCR high-throughput sequencing (HTS) has been used with similar panels to identify gene rearrangements. Both these methods have limitations, in that only rearrangements covered by the panels are detected, and the techniques are labour intensive and expensive. Whole genome sequencing (WGS) has potential to discover all IG/TCR rearrangements in a single assay giving greater potential to detect markers for follow up MRD monitoring. In England all patients with ALL are eligible for WGS at diagnosis via the National Health Service, Genomic Medicine Service (NHS GMS). Methods Sixty-two patients, presenting with ALL at either Great Ormond Street Hospital or Bristol Children's Hospital, had short-read WGS via the NHS GMS performed concurrently with standard of care (SOC) targeted HTS to identify IG/TCR rearrangements. A bespoke bioinformatic pipeline was developed to identify IG/TCR rearrangements in the WGS samples. SOC targeted HTS analysis was performed using the open source Vidjil bioinformatic software. A cut-off of 5% (clone proportion of total clonotypes) was used to define an acceptable disease-specific clone for monitoring. Results All 62 samples had a greater number of rearrangements detected by WGS than targeted HTS. The mean number of clones &amp;gt;5% detected was 2.7 by targeted HTS and 3.1 by WGS. For rearrangements identified by targeted HTS at a 5% cut-off, 97% (185 out of 190) were identified by WGS. All targets were identified by WGS using a 10% cut-off (Table 1).Despite the lower depth of coverage of the WGS assay, it retrieved 89% of sequences seen in SOC down to a level of 1%, as well as additional re-arrangements not seen by the SOC assay (Figure 1). As well as additional rearrangements being discovered by WGS, we observed novel rearrangements (dd3/ja rearrangements) not previously identified using a targeted approach. To establish if the additional sequences identified by WGS were clinically applicable as MRD targets, we designed allele specific oligonucleotides for a representative 32 rearrangements for testing by qPCR. Over half (n=16) were able to be used to track MRD in follow-up samples. New primer/probe sets were designed for the novel dd3/ja rearrangements identified by WGS, these were also tested by qPCR and performed well enough to be clinically applicable for MRD monitoring. Conclusions WGS approach outperformed targeted HTS, identifying more IG/TCR rearrangements suitable for molecular MRD monitoring in all patients. This is of particularly benefit in cases where targeted HTS only identifies 1 or 2 potential MRD targets. WGS also has the advantage of identifying other key gene rearrangements (BCR-ABL, KMT2A, SIL-TAL, IKZF1) that make excellent targets for MRD monitoring. Beyond this, WGS delivers all diagnostic information, germline and additional somatic findings that aid diagnosis. For ALL, in one test WGS can now replace all molecular diagnostics. In England all patients with leukemia are eligible for centralised WGS. With continued improvements in turnaround times, and reducing costs, we propose that WGS should be the new diagnostic standard for pediatric ALL

Automatic Thread Insertion Kit Sewing (ATIKs) Device for Vocational Fashion Design Student: Practical Sewing Application

The incorporation of technology is a consistent aspect of education, which is widely advanced through teaching and learning (T&amp;L) in the industrial revolution 4.0 (IR 4.0) education technology. In this study, an automatic thread insertion set for sewing (ATIKs) was developed. The ATIKs device improves students' understanding and facilitates the process of sewing in fashion design subjects for high school curriculum. The device was programmed using an Arduino microcontroller board and open source Arduino software (IDE). The CDIO model is used as the basis for product development, which includes the steps from conception to design, implementation, and operation. The development for this product consists of the main components standard needle size, automatic thread, Arduino UNO R3, stepper motor, coil, and Arduino coding to control the movement. In addition, this product is relatively portable with its dimensions of 7 x 7 x 10 cm. A qualitative research method was applied using a list of interview protocols validated by five professional experts in the field. Based on the open-ended questions, the experts indicated that this product facilitates the criteria of the T&amp;L process, improves understanding of the use of the thread needle, and increases student confidence in the sewing process. In the future, this kit could be integrated into an automated control system. This research development can help develop teaching tools that further increase student safety and improve student skills and cleanliness in handling sewing tools during hands-on work.

Europe PMC in 2023.

Europe PMC (https://europepmc.org/) is an open access database of life science journal articles and preprints, which contains over 42 million abstracts and over 9 million full text articles accessible via the website, APIsand bulk download. This publication outlines new developments to the Europe PMC platform since the last database update in 2020 (1) and focuses on five main areas. (i) Improving discoverability, reproducibilityand trust in preprints by indexing new preprint content, enriching preprint metadataand identifying withdrawn and removed preprints. (ii) Enhancing support for text and data mining by expanding the types of annotations provided and developing the Europe PMC Annotations Corpus, which can be used to train machine learning models to increase their accuracy and precision. (iii) Developing the Article Status Monitor tool and email alerts, to notify users about new articles and updates to existing records. (iv) Positioning Europe PMC as an open scholarly infrastructure through increasing the portion of open source core software, improving sustainability and accessibility of the service.

FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI

Abstract The hypothalamus plays a crucial role in the regulation of a broad range of physiological, behavioral, and cognitive functions. However, despite its importance, only a few small-scale neuroimaging studies have investigated its substructures, likely due to the lack of fully automated segmentation tools to address scalability and reproducibility issues of manual segmentation. While the only previous attempt to automatically sub-segment the hypothalamus with a neural network showed promise for 1.0 mm isotropic T1-weighted (T1w) magnetic resonance imaging (MRI), there is a need for an automated tool to sub-segment also high-resolutional (HiRes) MR scans, as they are becoming widely available, and include structural detail also from multi-modal MRI. We, therefore, introduce a novel, fast, and fully automated deep-learning method named HypVINN for sub-segmentation of the hypothalamus and adjacent structures on 0.8 mm isotropic T1w and T2w brain MR images that is robust to missing modalities. We extensively validate our model with respect to segmentation accuracy, generalizability, in-session test-retest reliability, and sensitivity to replicate hypothalamic volume effects (e.g., sex differences). The proposed method exhibits high segmentation performance both for standalone T1w images as well as for T1w/T2w image pairs. Even with the additional capability to accept flexible inputs, our model matches or exceeds the performance of state-of-the-art methods with fixed inputs. We, further, demonstrate the generalizability of our method in experiments with 1.0 mm MR scans from both the Rhineland Study and the UK Biobank—an independent dataset never encountered during training with different acquisition parameters and demographics. Finally, HypVINN can perform the segmentation in less than a minute (graphical processing unit [GPU]) and will be available in the open source FastSurfer neuroimaging software suite, offering a validated, efficient, and scalable solution for evaluating imaging-derived phenotypes of the hypothalamus.

Machine learning-based morphological quantification of replicative senescence in human fibroblasts.

Although aging has been investigated extensively at the organismal and cellular level, the morphological changes that individual cells undergo along their replicative lifespan have not been precisely quantified. Here, we present the results of a readily accessible machine learning-based pipeline that uses standard fluorescence microscope and open access software to quantify the minute morphological changes that human fibroblasts undergo during their replicative lifespan in culture. Applying this pipeline in a widely used fibroblast cell line (IMR-90), we find that advanced replicative age robustly increases (+28-79%) cell surface area, perimeter, number and total length of pseudopodia, and nuclear surface area, while decreasing cell circularity, with phenotypic changes largely occurring as replicative senescence is reached. These senescence-related morphological changes are recapitulated, albeit to a variable extent, in primary dermal fibroblasts derived from human donors of different ancestry, age, and sex groups. By performing integrative analysis of single-cell morphology, our pipeline further classifies senescent-like cells and quantifies how their numbers increase with replicative senescence in IMR-90 cells and in dermal fibroblasts across all tested donors. These findings provide quantitative insights into replicative senescence, while demonstrating applicability of a readily accessible computational pipeline for high-throughput cell phenotyping in aging research.