Step In Analysis Research Articles

Cooltools: Enabling high-resolution Hi-C analysis in Python.

Chromosome conformation capture (3C) technologies reveal the incredible complexity of genome organization. Maps of increasing size, depth, and resolution are now used to probe genome architecture across cell states, types, and organisms. Larger datasets add challenges at each step of computational analysis, from storage and memory constraints to researchers' time; however, analysis tools that meet these increased resource demands have not kept pace. Furthermore, existing tools offer limited support for customizing analysis for specific use cases or new biology. Here we introduce cooltools (https://github.com/open2c/cooltools), a suite of computational tools that enables flexible, scalable, and reproducible analysis of high-resolution contact frequency data. Cooltools leverages the widely-adopted cooler format which handles storage and access for high-resolution datasets. Cooltools provides a paired command line interface (CLI) and Python application programming interface (API), which respectively facilitate workflows on high-performance computing clusters and in interactive analysis environments. In short, cooltools enables the effective use of the latest and largest genome folding datasets.

Barriers to implementing problem-based learning at the school of medicine of Debre Berhan University, Ethiopia

BackgroundImplementing PBL in teaching and learning can be challenging due to a variety of complex barriers. Studies on barriers to the implementation of problem-based learning in Ethiopia are scarce. This study aimed to explore the barriers to the implementation of problem-based learning at the Debre Berhan University Medical School.MethodsA qualitative study was conducted among faculty and medical students at the medical school. Purposive sampling was used to select participants. Semi-structured interviews were conducted with tutors and academic leaders, including the problem-based learning coordinator, the biomedical sciences coordinator, and the school dean. Data was also collected from students through focus group discussions. All interviews and discussions were recorded. The four steps of data analysis of Spradley, including domain analysis, taxonomic analysis, componential analysis, and theme analysis, were employed.ResultsThe study identified student-related, tutor-related, case scenario-related, and assessment-related barriers as the most significant obstacles to implementing problem-based learning. These barriers included work overload for both students and tutors, lack of training and experience among tutors, student reluctance, absence of standardized case scenarios, subjectivity of assessment methods, and on-the-spot assessment of students.Conclusions and recommendations: Lack of both tutor and student commitment, lack of standardized cases, absence of a recognition of staff input, gap in communication skills, work overload, lack of continuous training, and at-spot evaluation of students were identified as the main barriers to the implementation of PBL.

Factor retention in ordered categorical variables: Benefits and costs of polychoric correlations in eigenvalue-based testing.

An essential step in exploratory factor analysis is to determine the optimal number of factors. The Next Eigenvalue Sufficiency Test (NEST; Achim, 2017) is a recent proposal to determine the number of factors based on significance tests of the statistical contributions of candidate factors indicated by eigenvalues of sample correlation matrices. Previous simulation studies have shown NEST to recover the optimal number of factors in simulated datasets with high accuracy. However, these studies have focused on continuous variables. The present work addresses the performance of NEST for ordinal data. It has been debated whether factor models-and thus also the optimal number of factors-for ordinal variables should be computed for Pearson correlation matrices, which are known to underestimate correlations for ordinal datasets, or for polychoric correlation matrices, which are known to be instable. The central research question is to what extent the problems associated with Pearson correlations and polychoric correlations deteriorate NEST for ordinal datasets. Implementations of NEST tailored to ordinal datasets by utilizing polychoric correlations are proposed. In a simulation, the proposed implementations were compared to the original implementation of NEST which computes Pearson correlations even for ordinal datasets. The simulation shows that substituting polychoric correlations for Pearson correlations improves the accuracy of NEST for binary variables and large sample sizes (N = 500). However, the simulation also shows that the original implementation using Pearson correlations was the most accurate implementation for Likert-type variables with four response categories when item difficulties were homogeneous.

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll

Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization–Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.

A New Model for Improving Quality of Hypertension’s Determinant Factors in Pregnancy

Background Hypertension in pregnancy should be detected earlier to prevent possible complications of pregnancy such as preeclampsia. Pregnancy hypertension could transform into preeclampsia if unmonitored and untreated, it can even be bad for eclampsia. Purpose This research aims to identify the determinant factors of hypertension during pregnancy and to develop a suitable model to prevent hypertension in the future. Methods The research was developed using a mixed method with an embedded design approach. This study has adopted the ADDIE concept including Analysis, Design, Development, Implementation, and Evaluation steps. Purposive sampling based on knowledge proportion has designated 164 pregnant women as key participants. Qualitative analysis was applied through in-depth interviews. Quantitative analysis was conducted with SEM-PLS. Results The determinant variable components of late detection of hypertension in pregnancy are knowledge and understanding, self-efficacy, intentions, and attitudes of pregnant women. Other variables are also considered as psychosocial support of husband/family and midwives, antenatal care, and service facilities. Conclusion The data shows that the MEGA model is proven to be effective in evaluating the determinant factors of hypertension in pregnancy.

Electron ionization mass spectrometry feature peak relationships combined with deep classification model to assist similarity algorithm for fast and accurate identification of compounds.

Gas chromatography-mass spectrometry (GC-MS) combines chromatography and MS, providing full play to the advantages of high separation efficiency of GC, strong qualitative ability of MS, and high sensitivity of detector. In GC-MS data processing, determining the experimental compounds is one of the most important analytical steps, which is usually realized by one-to-one similarity calculations between the experimental mass spectrum and the standard mass spectrum library. Although the accuracy of the algorithm has been improved in recent years, it is still difficult to distinguish structurally similar mass spectra, especially isomers. At the same time, the library capacity is very large and increasing every year, and the algorithm needs to perform large numbers of calculations with irrelevant compounds in the library to recognize unknown compounds, which leads to a significant reduction in efficiency. This work proposed to exclude a large number of irrelevant mass spectra by presearching, perform preliminary similarity calculations using similarity algorithms, and finally improve the accuracy of similarity calculations using deep classification models. The replica library of NIST17 is used as the query data, and the master library is used as the reference database. Compared with the traditional recognition algorithm, the preprocessing algorithm has reduced the time by 4.2h, and by adding the deep learning models 1 and 2 as the final determination, the recognition accuracy has been improved by 1.9% and 6.5%, respectively, based on the original algorithm. This method improves the recognition efficiency compared to conventional algorithms and at the same time has better recognition accuracy for structurally similar mass spectra and isomers.

Abstract PO4-22-02: pSmad2 as a marker of radiotherapy benefit in breast cancer- results from the randomized SweBCG91RT trial

Abstract Background: The TGF-beta pathway can have both pro- and antitumoral effects in breast cancer. Experimental studies further suggest that TGF-beta can mediate radioresistance, either through direct effects on tumor cells or indirectly by its immunomodulatory properties. However, the effect of TGF-beta signaling is highly depending on tumor stage, mutational status and microenvironmental cell composition. In order to explore TGF-beta signaling as a potential biomarker or therapeutical target it is therefore important to perform analyses in well-defined study populations. The purpose of this study was to study the effect of active TGF beta signaling in tumor cells, on the risk of ipsilateral breast tumor recurrence (IBTR) and benefit from radiotherapy (RT) in specifically early-stage breast cancer using nuclear, phosphorylated Smad2 (pSmad2) as a marker. Methods: The SweBCG91RT cohort included 1179 patients with stage I-IIA tumors randomized to breast-conserving surgery (BCS) with or without postoperative RT. Nuclear pSmad2 expression was assessed as for 987 available tumors on TMAs as the mean proportion of tumor cells with positive nuclear staining. Based on the distributions, tumors were categorized as having high (≥80%, n=344), medium (21-79%, n=428) or low (≤20%, n=161) pSmad2 staining. The primary endpoint was time to ipsilateral breast tumor recurrence as the first event within 10 years. Results: pSmad2Low tumors were more likely to be of grade III (p< 0.001) and tended to be larger than pSmad2Medium (p=0.002, median 15 vs 12 mm) and pSmad2High (p< 0.001, median 15 vs 12 mm) tumors. No associations between pSmad2 and subtype or age were found. Numerically, tumors with pSmad2low (HR 1.55, CI 95% 0.80-3.02, p=0.19) or pSmad2Medium (HR 2.53, CI 95% 1.52-4.22, p< 0.001) had a higher risk of IBTR than pSmad2high tumors (reference). These findings remained significant after adjustment for age, histological grade, and RT. The benefit from RT was similar for pSmad2High (HR0.41, CI 95% 0.15-1.07, p=0.068), pSmad2Medium (HR 0.36, CI 95% 0.21-0.63, p< 0.001), and pSmad2Low (HR 0.51, CI 95% 0.18-1.46, p=0.21) tumors (pinteraction=0.86). Conclusions: Early-stage breast tumors with high nuclear pSmad2 staining may be at a reduced risk of an IBTR. However, the relative benefit from RT does not seem to differ from tumors with medium or low pSmad2 staining. These results need to be confirmed in additional studies, but they provide initial evidence that tumoral TGF-beta signaling has rather tumor restraining functions in early-stage breast cancer. Next analytical steps should include the tumor mutation status (particularly for TGF-beta signaling components TGFBR2, SMAD4 and TP53), as well as the TGF-beta signaling status within the tumor microenvironment. Citation Format: Per Karlsson, Viktoria Thurfjell, Anikó Kovács, Fredrika Killander, Emma Niméus, Erik Holmberg, Carina Strell, Axel Stenmark Tullberg. pSmad2 as a marker of radiotherapy benefit in breast cancer- results from the randomized SweBCG91RT trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-22-02.

Genetic diversity and phylogeographic dynamics of avihepadnavirus: a comprehensive full-length genomic view

Avihepadnavirus is a genus of the Hepadnaviridae family. It primarily infects birds, including species of duck, geese, cranes, storks, and herons etc. To understand the genetic relatedness and evolutionary diversity among avihepadnavirus strains, a comprehensive analysis of the available 136 full-length viral genomes (n = 136) was conducted. The genomes were classified into two major genotypes, i.e., GI and GII. GI viruses were further classified into 8 sub-genotypes including DHBV-I (duck hepatitis B virus-I), DHBV-II (Snow goose Hepatitis B, SGHBV), DHBV-III, RGHBV (rossgoose hepatitis B virus), CHBV (crane hepatitis B virus), THBV (Tinamou hepatitis B virus), STHBV (stork hepatitis B virus), and HHBV (Heron hepatitis B virus). DHBV-I contains two sub-clades DHBV-Ia and DHBV-Ib. Parrot hepatitis B virus (PHBV) stains fall into GII which appeared as a separate phylogenetic branch/clade. All the subtypes of viruses in GI and GII seem to be genetically connected with viruses of DHBV-I by multiple mutational steps in phylogeographic analysis. Furthermore, 16 potential recombination events among different sub-genotypes in GI and one in GII were identified, but none of which is inter-genotypic between GI and GII. Overall, the results provide a whole picture of the genetic relatedness of avihepadnavirus strains, which may assist in the surveillance of virus spreading.

Isolation of nucleic acids using liquid–liquid phase separation of pH-sensitive elastin-like polypeptides

Extraction of nucleic acids (NAs) is critical for many methods in molecular biology and bioanalytical chemistry. NA extraction has been extensively studied and optimized for a wide range of applications and its importance to society has significantly increased. The COVID-19 pandemic highlighted the importance of early and efficient NA testing, for which NA extraction is a critical analytical step prior to the detection by methods like polymerase chain reaction. This study explores simple, new approaches to extraction using engineered smart nanomaterials, namely NA-binding, intrinsically disordered proteins (IDPs), that undergo triggered liquid–liquid phase separation (LLPS). Two types of NA-binding IDPs are studied, both based on genetically engineered elastin-like polypeptides (ELPs), model IDPs that exhibit a lower critical solution temperature in water and can be designed to exhibit LLPS at desired temperatures in a variety of biological solutions. We show that ELP fusion proteins with natural NA-binding domains can be used to extract DNA and RNA from physiologically relevant solutions. We further show that LLPS of pH responsive ELPs that incorporate histidine in their sequences can be used for both binding, extraction and release of NAs from biological solutions, and can be used to detect SARS-CoV-2 RNA in samples from COVID-positive patients.

A cautionary tale about properly vetting datasets used in supervised learning predicting metabolic pathway involvement.

The mapping of metabolite-specific data to pathways within cellular metabolism is a major data analysis step needed for biochemical interpretation. A variety of machine learning approaches, particularly deep learning approaches, have been used to predict these metabolite-to-pathway mappings, utilizing a training dataset of known metabolite-to-pathway mappings. A few such training datasets have been derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG). However, several prior published machine learning approaches utilized an erroneous KEGG-derived training dataset that used SMILES molecular representations strings (KEGG-SMILES dataset) and contained a sizable proportion (~26%) duplicate entries. The presence of so many duplicates taint the training and testing sets generated from k-fold cross-validation of the KEGG-SMILES dataset. Therefore, the k-fold cross-validation performance of the resulting machine learning models was grossly inflated by the erroneous presence of these duplicate entries. Here we describe and evaluate the KEGG-SMILES dataset so that others may avoid using it. We also identify the prior publications that utilized this erroneous KEGG-SMILES dataset so their machine learning results can be properly and critically evaluated. In addition, we demonstrate the reduction of model k-fold cross-validation (CV) performance after de-duplicating the KEGG-SMILES dataset. This is a cautionary tale about properly vetting prior published benchmark datasets before using them in machine learning approaches. We hope others will avoid similar mistakes.

Revisiting gas-chromatography/mass-spectrometry molar response factors for quantitative analysis (FID or TIC) of glycosidic linkages in polysaccharides produced by oral bacterial biofilms.

Methylation analysis was performed on methylated alditol acetate standards and Streptococcus mutans extracellular polymeric substances (EPS) produced from wild-type and Gtf knockout strains (∆GtfB, ∆GtfB, and ∆GtfD). The methylated alditol acetate standards were representative of glycosidic linkages found in S. mutans EPS and were used to calibrate the GC-MS system for an FID detector and MS (TIC) and produce molar response factor, a necessary step in quantitative analysis. FID response factors were consistent with literature values (Sweet et al., 1975) and found to be the superior option for quantitative results, although the TIC response factors now give researchers without access to an FID detector a needed option for molar response factor correction. The GC-MS analysis is then used to deliver the ratio of the linkage types within a biofilm.

Comparing nitric acid treatment and microwave digestion for efficiency of metal extraction from bioprocess samples.

Metal ions may act as enzyme cofactors and influence the kinetics of biochemical reactions that may also influence the biological production of therapeutic proteins and quality attributes such as glycosylation. Because sample preparation is a significant step in the reliable analysis of metals, we compared two sample preparation procedures for metal analysis of bioreactor culture media samples by ICP-MS: (i) samples were diluted in 2 % nitric acid (treatment with nitric acid, TNA); and (ii) samples were mixed with equal volume of 5 % nitric acid and closed vessel digestion was performed in a microwave (closed vessel digestion, CVD). In the comparison of extraction efficiencies between TNA and CVD procedures, CVD showed better extraction for Ca and Cu among bulk metals (∼30%) and for Ni among the trace metals (∼65%) for the bioreactor broth supernatant samples. For the cell pellet samples, the CVD procedure was found to be better for extraction of Fe (∼65% more) among bulk metals, Zn (∼20% more) among minor metals and Co (∼60% more) and Ni (∼45% more) among trace metals. Differences between the two procedures were less than 10% and TNA was better for all other metals quantified from both supernatant samples and cell pellet samples. The current study helps bring more clarity to the methodology on comprehensive metal analysis to monitor and maintain trace metal content for biologics production.

CLIPPER 2.0: Peptide level annotation and data analysis for positional proteomics.

Positional proteomics methodologies have transformed protease research, and have brought mass spectrometry (MS)-based degradomics studies to the forefront of protease characterization and system-wide interrogation of protease signaling. Considerable advancements in both sensitivity and throughput of liquid chromatography (LC)-MS/MS instrumentation enable the generation of enormous positional proteomics datasets of natural and protein termini and neo-termini of cleaved protease substrates. However, a concomitant progress has not been observed to the same extent in data analysis and post-processing steps, arguably constituting the largest bottleneck in positional proteomics workflows. Here, we present a computational tool, CLIPPER 2.0, that builds on prior algorithms developed for MS-based protein termini analysis, facilitating peptide level annotation and data analysis. CLIPPER 2.0 can be used with several sample preparation workflows and proteomics search algorithms, and enables fast and automated database information retrieval, statistical and network analysis, as well as visualization of terminomic datasets. We demonstrate the applicability of our tool by analyzing GluC and MMP9 cleavages in HeLa lysates. CLIPPER 2.0 is available at https://github.com/UadKLab/CLIPPER-2.0.

Rejection Analysis of Piston - A Case Study

Abstract: Rejection analysis is a crucial aspect of quality management in the manufacturing industry, aimed at identifying and rectifying the root causes of rejected or defective products. This paper provides an overview of the concept, objectives, and methodologies involved in rejection analysis. The primary objective is to improve product quality by systematically analyzing rejected items and implementing corrective actions to prevent recurrence. Key steps in rejection analysis include identifying rejected products, conducting root cause analysis, collecting and analyzing data, implementing corrective actions, and monitoring for continuous improvement. Additionally, rejection analysis enables manufacturers to meet regulatory standards, mitigate risks, and foster a culture of continuous improvement. By emphasizing the importance of rejection analysis in driving quality, efficiency, and competitiveness, underscores its critical role in ensuring the success and sustainability of manufacturing operations.

Unveiling spatial insights: navigating the parameters of dynamic Geographic Information Systems (GIS) analysis

In today's rapidly evolving landscape of Geographic Information Systems (GIS), understanding the intricate parameters that govern GIS analysis is paramount. This title encapsulates the essence of delving into the multifaceted dimensions of GIS, where a myriad of factors converges to shape the process and outcomes of spatial analysis. From the selection of input data and spatial layers to the fine-tuning of analytical techniques and model parameters, every step in GIS analysis is governed by a set of parameters that influence the accuracy, reliability, and relevance of the results. The title beckons researchers, practitioners, and enthusiasts alike to embark on a journey of exploration, unraveling the complexities of GIS analysis and its underlying parameters. It speaks to the dynamic nature of GIS, where the interplay of spatial data, computational algorithms, and user-defined parameters creates a rich tapestry of insights and discoveries. Whether mapping urban growth patterns, assessing natural resource availability, or modeling environmental change, understanding the parameters of GIS analysis is essential for unlocking the full potential of spatial data and informing decision-making processes. Moreover, the title hints at the transformative power of GIS as a tool for understanding and addressing complex spatial phenomena. By scrutinizing the parameters of GIS analysis, researchers can gain deeper insights into the underlying processes driving spatial patterns and trends. They can identify optimal parameter settings, refine analytical workflows, and enhance the accuracy and precision of spatial models and predictions. In essence, "Exploring the Parameters: Understanding the Dynamics of GIS Analysis" encapsulates the essence of GIS as a dynamic and evolving field, where the exploration of parameters serves as a gateway to unlocking the full potential of spatial analysis and harnessing the power of geography to address real-world challenges and opportunities.

Breast Cancer Tumour Masks Segmentation Using Modified U-Net with Channel Attention

Abstract: A modified U-Net for biomedical image segmentation with channel attention is developed for breast cancer image segmentation. A unique step in biomedical image analysis is segmentation because it determines the accuracy of image analysis algorithms. Algorithms for accurate and early breast cancer detection in women using mammograms depend highly on the accuracy of the segmentation stage. In this thesis, in order to improve on the segmentation ability of the traditional UNet architecture, it was modified by varying the fixed kernel sizes that it was originally designed for. In this model, progressively increasing kernel sizes of 3x3, 5x5, 7x7, 9x9 and 11x11 were used, which is different from the architecture of UNet that depended on fixed 3x3 kernel sizes for all convolutional layers. The first Convolutional block of the proposed model contains two Conv2D layers with 3x3 kernel sizes, ReLU (Rectified Linear Unit) activation function, and 16 channel dimensions. A maxpooling2D layer with pool size of (2x2) was placed to downsample the image size before feeding to the next convolutional block which used the same activation function, with 32 channel dimension and 5x5 kernel size. The next convolutional block used 64 channel dimensions with 7x7 kernel size, then a 9x9 kernel size Conv2D block with 128 channel dimensions was placed before the Conv2D with upsampling. The squeeze and excitation block was placed after this convolutional layer and in-between the two convolutional blocks with 11x11 kernel sizes, to reallocate the weight of the essential features to ensure that more discriminate features are learnt from the breast cancer images to improve the overall representational strength of the network through the performance of dynamic feature recalibration of the image channels. This significantly improved the performance of the segmentation by explicitly modeling the interdependencies between channels in the convolutional layers. The increasing kernel sizes enables the model to learn more discriminate features from the images. Jaccard similarity index, F1 score, Recall, Precision, F2 score, and accuracy results were used to compare the result of the proposed model with other models such as U-Net model and Baseline model. The results shows a better performance of 0.88 for Jaccard similarity index, 0.94 for F1 score, 0.93 for recall, 0.92 for precision, 0.92 for F2 score, and an accuracy score of 0.98%. The model is good for biomedical image segmentation.

The effect of data transformation on low-dimensional integration of single-cell RNA-seq

BackgroundRecent developments in single-cell RNA sequencing have opened up a multitude of possibilities to study tissues at the level of cellular populations. However, the heterogeneity in single-cell sequencing data necessitates appropriate procedures to adjust for technological limitations and various sources of noise when integrating datasets from different studies. While many analysis procedures employ various preprocessing steps, they often overlook the importance of selecting and optimizing the employed data transformation methods.ResultsThis work investigates data transformation approaches used in single-cell clustering analysis tools and their effects on batch integration analysis. In particular, we compare 16 transformations and their impact on the low-dimensional representations, aiming to reduce the batch effect and integrate multiple single-cell sequencing data. Our results show that data transformations strongly influence the results of single-cell clustering on low-dimensional data space, such as those generated by UMAP or PCA. Moreover, these changes in low-dimensional space significantly affect trajectory analysis using multiple datasets, as well. However, the performance of the data transformations greatly varies across datasets, and the optimal method was different for each dataset. Additionally, we explored how data transformation impacts the analysis of deep feature encodings using deep neural network-based models, including autoencoder-based models and proto-typical networks. Data transformation also strongly affects the outcome of deep neural network models.ConclusionsOur findings suggest that the batch effect and noise in integrative analysis are highly influenced by data transformation. Low-dimensional features can integrate different batches well when proper data transformation is applied. Furthermore, we found that the batch mixing score on low-dimensional space can guide the selection of the optimal data transformation. In conclusion, data preprocessing is one of the most crucial analysis steps and needs to be cautiously considered in the integrative analysis of multiple scRNA-seq datasets.

Residual behavior of UHPC-filled Q690 high-strength steel tubular members after axial impact

Structures may suffer from accidental impact loads during the service life. While most impacts will not destroy structures completely, quantifying their residual behavior after impact is crucial for evaluating the damage and then providing recommendations for repair or removal. This may greatly reduce the time and economic losses caused by impacts. Hence, this study experimentally and numerically investigated the residual behavior of UHPC-filled high-strength steel tube (UHPC-FHST) members subjected to axial impact. Parameters including impact energy, steel ratio, specimen length, and steel grade (Q690 and Q355) were carefully considered. After tests, different failure modes and axial load-displacement curves were obtained. The residual ratio (β), a ratio of the bearing capacity of a damaged specimen to that of a specimen without impact, was employed to assess the damage level of UHPC-FHST members after axial impact. Tests showed that UHPC-FHST members have excellent axial impact resistance, high residual strength, and high residual ratio, especially under low-energy impact conditions. β decreases rapidly and linearly with increasing nominal strain and then stabilizes after a certain value. A finite element (FE) model was thereafter established and benchmarked by the available test results, where two analysis steps were established to continuously simulate the impact and residual compression processes. The FE results demonstrated a great agreement with the tests, which could provide a reference for further investigations in related studies.

A Qualitative Inquiry Into the Occupational Challenges Faced by Home-Based Caregivers of People Living With HIV in South Africa’s Gauteng Province

Community home-based caregivers caring for people living with human immunodeficiency virus (PLWHIV) are instrumental in relieving the demand pressures experienced by healthcare service centers. Although their contributions to the healthcare system are essential, they do so under difficult conditions necessitating social work support. This study was conducted in the Gauteng province of South Africa in 2017 to explore the occupational challenges faced by community home-based care caregivers of PLWHIV. Following the qualitative research approach, eighteen caregivers were identified and sampled using purposive and snowball sampling techniques in semi-structured interviews. The data was manually analyzed according to Tesch’s eight steps of qualitative data analysis. This gave rise to four themes: reception by community members, uncooperative clients and families, unsupportive healthcare professionals, and working in unsafe conditions. Following relevant ethical principles such as informed consent, confidentiality, and anonymity throughout the study, trustworthiness was continuously upheld through the principles of prolonged engagement, triangulation, member checking, thick description, and audit trail. The need for psychosocial support programs for caregivers, community and family educational programs, and strengthening partnerships for caregivers, communities, and healthcare professionals is recommended, given the success of community home-based care (CHBC) services for PLWHIV.

AST: An OpenSim-based tool for the automatic scaling of generic musculoskeletal models

Background and objectivesThe paper introduces a tool called Automatic Scaling Tool (AST) designed for improving and expediting musculoskeletal (MSK) simulations based on generic models in OpenSim. Scaling is a crucial initial step in MSK analyses, involving the correction of virtual marker locations on a model to align with actual experimental markers. MethodsThe AST automates this process by iteratively adjusting virtual markers using scaling and inverse kinematics on a static trial. It evaluates the root mean square error (RMSE) and maximum marker error, implementing corrective actions until achieving the desired accuracy level. The tool determines whether to scale a segment with a marker-based or constant scaling factor based on checks on RMSE and segment scaling factors. ResultsTesting on three generic MSK models demonstrated that the AST significantly outperformed manual scaling by an expert operator. The RMSE for static trials was one order of magnitude lower, and for gait tasks, it was five times lower (8.5 ± 0.76 mm vs. 44.5 ± 7.5 mm). The AST consistently achieved the desired level of accuracy in less than 100 iterations, providing reliable scaled MSK models within a relatively brief timeframe, ranging from minutes to hours depending on model complexity. ConclusionsThe paper concludes that AST can greatly benefit the biomechanical community by quickly and accurately scaling generic models, a critical first step in MSK analyses. Further validation through additional experimental datasets and generic models is proposed for future tests.