Development Of Model Research Articles

Understanding Team Dynamics and Culture of Safety using Video Reflexive Ethnography during Real-Time Emergent Intubation.

Endotracheal intubation is the third most common bedside procedure in U.S. hospitals. In over 40% of intubations preventable complications attributable to human factors occur. A better understanding of team dynamics during intubation may improve patient safety. To explore team dynamics and safety-related actions during emergent endotracheal intubations in the emergency department (ED) and intensive care unit (ICU), and to engage members of the care team in reflection for process improvement through a novel video-based team debriefing technique. Video-reflexive ethnography involves in-situ video-recording and reflexive discussions with practitioners to scrutinize behaviors and to identify opportunities for improvement. In this study, real-time intubations were recorded in the ED and ICU at Mayo Clinic Rochester and facilitated video-reflexive sessions were conducted with the multidisciplinary procedural teams. Themes about team dynamics and safety-related action were identified inductively from transcriptions of recorded sessions. Between December 2022 and January 2023, eight video-reflexive sessions were conducted with a total of 78 participants. Multidisciplinary members included nurses (n=23), respiratory therapists (n=16), pharmacists (n=7), advanced practitioners (n=5), and physicians (n=26). Video-reflexive discussions identified major safety gaps and proposed several solutions related to the use of a multidisciplinary intubation checklist, standardized communication and team positioning, developing a culture of safety, and routinely debriefing after the procedure. The findings of this study may inform the development of a team supervision model for emergent endotracheal intubations. This approach could integrate key components such as a multidisciplinary intubation checklist, standardized communication and team positioning, a culture of safety, and debriefing as part of the procedure itself.

A New Macro-Element for Predicting the Behavior of Masonry Structures under In-Plane Cyclic Loading

A new macro model for the finite element modeling of unreinforced masonry (URM) exhibiting in-plane nonlinear cyclic behavior is proposed. The ultimate objective is to predict the seismic response of multi-story URM buildings. The macro model enables the modeling of URM shear walls with a limited number of degrees of freedom (DOF) at low computation times. The macro model consists of a deformable elastic frame supported by diagonal struts with nonlinear behavior aiming to capture all dissipative phenomena occurring during seismic events. The nonlinear constitutive behavior of diagonal struts is inspired by models documented in the literature, ensuring a robust foundation for the proposed approach. This paper first provides a comprehensive review of the principal models currently available for URM analysis. It then articulates the rationale behind the development of this new numerical model, aiming to address the limitations encountered in existing methodologies and to offer a simple and fast tool for predicting the seismic behavior of URM buildings. Afterward, the new model is presented and tested with the simulations of two experimental campaigns performed on different URM walls. The comparison between experimental and numerical results shows that with a limited number of DOF and parameters, it is possible to obtain a prediction of the experimental results with satisfying accuracy.

Development of a prognostic model for lung adenocarcinoma polarity-related genes and analysis of immune landscape.

Despite the progress made in the management of lung adenocarcinoma (LUAD), the overall prognosis for LUAD individuals remains suboptimal. While the role of cell polarity in tumor invasion and metastasis is well established, its prognostic significance in LUAD is still unknown. Differential analysis was performed on the Cancer Genome Atlas (TCGA)-LUAD and normal lung tissue, and candidate genes were identified by intersecting differentially expressed genes with polarity-related genes (PRGs). A prognostic model was constructed using univariate and multivariate Cox regression and LASSO regression. To enhance the robustness of the analysis, an independent prognostic analysis was conducted by incorporating relevant clinical information. The accuracy and sensitivity of the model were validated using survival analysis and ROC curves. Finally, immune landscape, immune therapy, tumor mutation burden, and drug sensitivity analysis were carried out on high- and low-risk patients. Ten prognostic genes were screened to divide LUAD patients into different risk groups. Survival analysis, ROC curves, and univariate/multivariate Cox regression analyses collectively demonstrated the favorable predictive performance of the model, which could be an independent prognostic factor. The nomogram, in conjunction with the calibration curve, demonstrated the model's compelling predictive capacity in prognosticating the overall survival of LUAD individuals. Low-risk LUAD patients exhibited heightened levels of immune cell infiltration, immune scores, and immune checkpoint expression compared to high-risk individuals. So, they may have a greater likelihood of benefiting from immune therapy. The high-risk group demonstrated a remarkably higher tumor mutation burden (TMB) in contrast with the low-risk group. XAV-939, Fulvestrant, and SR16157 may have potential value in the clinical use of LUAD. We revealed the potential linkage between PRGs and LUAD prognosis, and the application of these prognostic factors in risk stratification and prognosis prediction of LUAD patients may be of great significance.

Modelling the unsteady lift of a pitching NACA 0018 aerofoil using state-space neural networks

The development of simple, low-order and accurate unsteady aerodynamic models represents a crucial challenge for the design optimisation and control of fluid dynamical systems. In this work, wind tunnel experiments of a pitching NACA 0018 aerofoil conducted at a Reynolds number $Re = 2.8 \times 10^5$ and at different free-stream turbulence intensities are used to identify data-driven nonlinear state-space models relating the time-varying angle of attack of the aerofoil to the lift coefficient. The proposed state-space neural network (SS-NN) modelling technique explores an innovative methodology, which brings the flexibility of artificial neural networks into a classical state-space representation and offers new insights into the construction of reduced-order unsteady aerodynamic models. The work demonstrates that this technique provides accurate predictions of the nonlinear unsteady aerodynamic loads of a pitching aerofoil for a wide variety of angle-of-attack ranges and frequencies of oscillation. Results are compared with a modified version of the Goman–Khrabrov dynamic stall model. It is shown that the SS-NN methodology outperforms the classical semi-empirical dynamic stall models in terms of accuracy, while retaining a fast evaluation time. Additionally, the proposed models are robust to noisy measurements and do not require any pre-processing of the data, thus involving only a limited user interaction. Overall, these features make the SS-NN technique an excellent candidate for the construction of accurate data-driven models from experimental fluid dynamics data, and pave the way for their adoption in applications entailing design optimisation and real-time control of systems involving lift.

BRIDGING PANDEMICS AND PIXELS: A COMPREHENSIVE BIBLIOMETRIC ANALYSIS OF DEEP LEARNING APPLICATIONS IN COVID-19 DETECTION

The world has been significantly impacted by the global pandemic of COVID-19, leading researchers to explore various methods for detecting the virus. Deep learning (DL) technologies have emerged as pivotal tools in detecting and managing the virus. This review article aims to provide a comprehensive examination of the advancements and applications of DL in the context of COVID-19 detection, offering insights into the evolution, impact, and future direction of this rapidly evolving field. A thorough bibliometric review was carried out using the Scopus database. The methodology involved keyword-based searches, analyses of publication trends, and studies of co-citation networks, with a focus on literature from 2020 to 2023. Data visualisation tools, particularly VOSviewer, were used to analyse and map bibliometric data, highlighting publication trends, and authorship patterns. The study found a substantial rise in DL research for detecting COVID-19 from 2020 to 2023, with significant input from countries such as India, China, and Saudi Arabia. The trends in research are expected to showcase developments in DL models for medical imaging, including CT scans and X-rays, as well as the increasing significance of AI in medical diagnostics. The study also pinpointed the main collaborative networks and popular keywords in this research area. This bibliometric analysis aims to lay the foundation for future research directions by offering a comprehensive overview of the evolution of DL in COVID-19 detection It will provide strategic insights and research directions to advance this vital domain at the intersection of computational intelligence and global health.

Interdependencies in Electrode Manufacturing: A Comprehensive Study Based on Design Augmentation and Explainable Machine Learning

AbstractElectrode manufacturing, as the core of battery cell production, is a complex process chain with a large number of interrelated parameters. An in‐depth understanding of the processes, their relevant parameters, and the resulting effects on intermediate and final product properties can accelerate the transition toward quality‐oriented, efficient battery cell production. Given the complexity of the process chain, data‐driven models have emerged as promising solutions for analyzing the existing interdependencies. The accuracy and effectiveness of these models significantly depend on the quality and comprehensiveness of the underlying data. With a low‐quality dataset, there is an increased risk of drawing inaccurate conclusions or generating misleading results. This article aimed to demonstrate a use case for the evaluation and enhancement of historical datasets to provide a statistically robust foundation for the development of machine learning models. The study was based on pilot‐scale anode manufacturing and covered variations in the coating, drying, and calendering processes. The key intermediate product and process parameters were used to predict two primary target variables: adhesion strength and discharge capacity at different C‐rates. To gain a better understanding of the analyzed interdependencies, explainable machine learning methods were adopted.

Microphysiological Blood-Brain Barrier Systems for Disease Modeling and Drug Development.

The blood-brain barrier (BBB) is a highly controlled microenvironment that regulates the interactions between cerebral blood and brain tissue. Due to its selectivity, many therapeutics targeting various neurological disorders are not able to penetrate into brain tissue. Pre-clinical studies using animals and other in vitro platforms have not shown the ability to fully replicate the human BBB leading to the failure of a majority of therapeutics in clinical trials. However, recent innovations in vitro and ex vivo modeling called organs-on-chips have shown the potential to create more accurate disease models for improved drug development. These microfluidic platforms induce physiological stressors on cultured cells and are able to generate more physiologically accurate BBBs compared to previous in vitro models. In this review, different approaches to create BBBs-on-chips are explored alongside their application in modeling various neurological disorders and potential therapeutic efficacy. Additionally, organs-on-chips use in BBB drug delivery studies is discussed, and advances in linking brain organs-on-chips onto multiorgan platforms to mimic organ crosstalk are reviewed.

Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms.

This study focused on the development and validation of a diagnostic model to differentiate between spinal tuberculosis (STB) and pyogenic spondylitis (PS). We analyzed a total of 387 confirmed cases, out of which 241 were diagnosed with STB and 146 were diagnosed with PS. These cases were randomly divided into a training group (n = 271) and a validation group (n = 116). Within the training group, four machine learning (ML) algorithms (least absolute shrinkage and selection operator [LASSO], logistic regression analysis, random forest, and support vector machine recursive feature elimination [SVM-RFE]) were employed to identify distinctive variables. These specific variables were then utilized to construct a diagnostic model. The model's performance was subsequently assessed using the receiver operating characteristic (ROC) curves and the calibration curves. Finally, internal validation of the model was undertaken in the validation group. Our findings indicate that PS patients had an average platelet-to-neutrophil ratio (PNR) of 277.86, which was significantly higher than the STB patients' average of 69.88. The average age of PS patients was 54.71 years, older than the 48 years recorded for STB patients. Notably, the neutrophil-to-lymphocyte ratio (NLR) was higher in PS patients at 6.15, compared to the 3.46 NLR in STB patients. Additionally, the platelet volume distribution width (PDW) in PS patients was 0.2, compared to 0.15 in STB patients. Conversely, the mean platelet volume (MPV) was lower in PS patients at an average of 4.41, whereas STB patients averaged 8.31. Hemoglobin (HGB) levels were lower in PS patients at an average of 113.31 compared to STB patients' average of 121.64. Furthermore, the average red blood cell (RBC) count was 4.26 in PS patients, which was less than the 4.58 average observed in STB patients. After evaluation, seven key factors were identified using the four ML algorithms, forming the basis of our diagnostic model. The training and validation groups yielded area under the curve (AUC) values of 0.841 and 0.83, respectively. The calibration curves demonstrated a high alignment between the nomogram-predicted values and the actual measurements. The decision curve indicated optimal model performance with a threshold set between 2% and 88%. In conclusion, our model offers healthcare practitioners a reliable tool to efficiently and precisely differentiate between STB and PS, thereby facilitating swift and accurate diagnoses.

Unraveling Spatial–Temporal Patterns and Heterogeneity of On-Ramp Vehicle Merging Behavior: Evidence from the exiD Dataset

Understanding the spatiotemporal characteristics of merging behavior is crucial for the advancement of autonomous driving technology. This study aims to analyze on-ramp vehicle merging patterns, and investigate how various factors, such as merging scenarios and vehicle types, influence driving behavior. Initially, a framework based on a high-definition (HD) map is developed to extract trajectory information in a meticulous manner. Subsequently, eight distinct merging patterns are identified, with a thorough examination of their behavioral characteristics from both temporal and spatial perspectives. Merging behaviors are examined temporally, encompassing the sequence of events from approaching the on-ramp to completing the merge. This study specifically analyzes the target lane’s spatial characteristics, evaluates the merging distance (ratio), investigates merging speed distributions, compares merging patterns and identifies high-risk situations. Utilizing the latest aerial dataset, exiD, which provides HD map data, the study presents novel findings. Specifically, it uncovers patterns where the following vehicle in the target lane chooses to accelerate and overtake rather than cutting in front of the merging vehicle, resulting in Time-to-Collision (TTC) values of less than 2.5 s, indicating a significantly higher risk. Moreover, the study finds that differences in merging speed, distance, and duration can be disregarded in patterns where vehicles are present both ahead and behind, or solely ahead, suggesting these patterns could be integrated for simulation to streamline analysis and model development. Additionally, the practice of truck platooning has a significant impact on vehicle merging behavior. Overall, this study enhances the understanding of merging behavior, facilitating autonomous vehicles’ ability to comprehend and adapt to merging scenarios. Furthermore, this research is significant in improving driving safety, optimizing traffic management, and enabling the effective integration of autonomous driving systems with human drivers.

Reservoir Static Modelling towards Safe CO2 Storage in Depleted Oil Reservoirs of ‘CRK’ Field, Niger Delta, Nigeria

Geological carbon storage (GCS) is gradually gaining acceptance as the technology of highest repute for the mitigation against greenhouse gas effect. This involves the injection and long-term or permanent storage of carbon dioxide (CO2) in subsurface geological formations. The development of a robust 3D reservoir model then becomes necessary to understand the facies changes and the petrophysical properties distribution of candidate CO2 storage reservoirs. This study attempts the use of static modelling technique to investigate the depleted oil and gas reservoirs of ‘CRK’ field in the Niger Delta sedimentary Basin as a potential CO2 storage site. Using an integrated approach of 3D seismic and a suit of well logs from the study area, two reservoir sands (D10C0 and D6200) were mapped. 3D static modelling of discrete and continuous property distribution of the reservoirs revealed the reservoirs are composed majorly of clean sands with water saturation ranging from 8 to 75%. The average porosity and permeability values are between 20 to 29% and 250 to 890mD respectively. A theoretical storage capacity of 24.85Mt is estimated for the two reservoirs combined, while the median effective storage capacity is 6.22Mt. Comparison of the results obtained in the study with recommended standard values show the reservoirs are suitable for CO2 storage. The property models constructed can serve as primary input for dynamic simulation of the oilfield in future studies.

Identifying immune-related prognostic biomarkers in osteosarcoma: Development and validation of the tumor immune microenvironment risk model based on analysis of TCGA.

Osteosarcoma is a rare and aggressive malignancy with limited effective therapeutic options. This study aimed to identify immune-related prognostic biomarkers and develop a prognostic model for osteosarcoma. We performed integrated analysis of transcriptomic data and immune cell infiltration profiles of 84 osteosarcoma samples from the Cancer Genome Atlas (TCGA) database. Time-dependent receiver operating characteristic (ROC) curve analysis was used to assess the prognostic value of the TIMErisk model. We also performed functional annotation and pathway enrichment analyses to explore the potential mechanisms underlying the TIMErisk model. We identified a seven-gene TIMErisk model (C2, APBB1IP, BST2, TRPV2, CCL5, GBP1, and F13A1) that was independently associated with overall survival of osteosarcoma patients. The TIMErisk model showed significant associations with immune cell infiltration and immunosuppressive gene expression. In addition, the TIMErisk model was associated with drug sensitivity, and we found that several immune checkpoint genes were significantly differentially expressed between high- and low-TIMErisk groups. Functional annotation and pathway enrichment analyses revealed that the TIMErisk model was associated with multiple immune-related pathways, including antigen processing and presentation, cytokine-cytokine receptor interaction, and T cell receptor signaling pathway. Our study identified a novel TIME-based prognostic model for osteosarcoma that incorporates immune-related genes and can be used to predict patient prognosis and response to immunotherapy. Our findings highlight the importance of the TIME microenvironment in osteosarcoma progression and suggest that immune-related biomarkers may have clinical significance in the management of osteosarcoma.

Assessment of Benilde Associates towards a Dynamic Business Continuity Model in Disruptive Times

Business continuity and disaster recovery planning are comprised of activities that an organization must perform to resume operations in an unexpected event. The De La Salle College of Saint Benilde SMIT research study adopted the Business Continuity Model and identified gaps that will lead to the proposed Dynamic Business Continuity Model in disruptive times. From this problem, the research question is to what extent BCM factors impact levels (depth and breadth) of the Business Continuity Model during disruptive times. Data was collected by surveying Benilde associates in various academic positions and the Office Center. The data collected was treated using Exploratory Factor Analysis and Multiple Regression for causal relationships. DLS CSB SMIT identified gaps and challenges for faculty and staff in several dimensions in the existing Business Continuity Model factors and the ff are: a. Systems/Data Recovery b. Organizational Support c. Employee Engagement d. Forecasting Capacity and among the identified gaps- “Organizational Support ‘ is considered the most significant among the 4 factors. This consists of Student Support Services, Hunan Resource, and Registrar’s Office. To prevent the interruption in DLS CSB SMIT operations and the survival of an organization during disruptive times, Transparency of the BCM Strategy cascaded to Benilde associates, the importance of creating awareness and being prepared for the unexpected. Business Continuity Model is an essential element that should form part of every critical business process within the College. It is imperative to understand the factors that were identified as gaps that the Institution could be faced with and also understand the steps that should be taken to address and manage these risks. This dissertation paper led to the development of a Dynamic Business Continuity Model which is essential to prepare DLS CSB for any disaster or crisis. The Model serves as a guide for all Centers on what they should do in such an event to ensure that the organizations will survive during the fortuitous event and how to restore the Business and Institution to normalcy. This Research study could provide a Business Continuity Model framework for leaders to assess their organization’s capacity from an unexpected disruption and be used by organizational leaders to assign contingency planning responsibilities or hire business continuity and disaster recovery planning professionals. Keywords: Business Continuity, Disruptive, Dynamic Business Model, Systems/Data Recovery, Organizational Support, Employee Engagement, Forecasting Capacity

Development of a rock-bit interaction analytical model by considering the in-situ stresses for a bottom-hole element

PDC drill bits are an important part of drilling engineering, but improper selection or design can lead to decreased performance and increased costs. Then, accurate modeling of rock-bit interaction for Oil/gas well drilling is critical. Although several mathematical models are presented for this purpose, they have not been able to present a comprehensive model for the rock-bit interaction. In-situ stresses in real drilling conditions affect the force required for rock failure. However, the models proposed so far either have not considered the effects of in-situ stresses or have assumed that the rock failure angle in the downhole conditions is equal to the one calculated in the atmospheric conditions. In this work, after reviewing the background of studies conducted on the rock and bit interaction, with an analytical method, stresses applied to the bottom hole element are examined, including stresses resulting from bit and in-situ stresses. Based on the principle of superposition, the total stress imposed on the bottom hole element is calculated to determine the angle and force of rock cutting. Finally, a novel mathematical model of rock-bit interaction in vertical and deviated oil/gas wells drilling by Considering In-Situ Stresses is presented. Also, the study compares the current model to the Nishimatsu and Xin Ling models using data from a southwest field in Iran. The results show that the simplifying assumption made by previous models leads to a significant underestimation of the failure angle and the amount of force required to the rock failure, with reductions of up to 21% and 48%, respectively, in the case of a vertical well. In an inclined well, the current model predicts cutting force at about 0.14 of that predicted by the previous model.

Articular cartilage corefucosylation regulates tissue resilience in osteoarthritis.

This study aimed to investigate the glycan structural changes that occur before histological degeneration in osteoarthritis (OA) and to determine the mechanism by which these glycan conformational changes affect cartilage degeneration. An OA model was established in rabbits using mannosidase injection, which reduced high-mannose type N-glycans and led to cartilage degeneration. Further analysis of glycome in human OA cartilage identified specific corefucosylated N-glycan expression patterns. Inhibition of N-glycan corefucosylation in mice resulted in unrecoverable cartilage degeneration, while cartilage-specific blocking of corefucosylation led to accelerated development of aging-associated and instability-induced OA models. We conclude that α1,6 fucosyltransferase is required postnatally to prevent preosteoarthritic deterioration of articular cartilage. These findings provide a novel definition of early OA and identify glyco-phenotypes of OA cartilage, which may distinguish individuals at higher risk of progression.

Development and validation of a risk prediction model for preterm birth in women with gestational diabetes mellitus.

This study aims to develop and validate a prediction model for preterm birth in women with gestational diabetes mellitus (GDM). We conducted a retrospective study on women with GDM who gave birth at the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, between November 2017 and July 2021. We divided 1879 patients into a development set (n = 1346) and a validation set (n = 533). The development set was used to construct the prediction model for preterm birth using the stepwise logistic regression model. A nomogram and a web calculator were established based on the model. Discrimination and calibration were assessed in both sets. Patients were women with GDM. Data were collected from medical records. GDM was diagnosed with 75-g oral glucose tolerance test during 24-28 gestational weeks. Preterm birth was definied as gestational age at birth <37 weeks. The incidence of preterm birth was 9.4%. The predictive model included age, assisted reproductive technology, hypertensive disorders of pregnancy, reproductive system inflammation, intrahepatic cholestasis of pregnancy, high-density lipoprotein, homocysteine, and fasting blood glucose of 75-g oral glucose tolerance test. The area under the receiver operating characteristic curve for the development and validation sets was 0.722 and 0.632, respectively. The model has been adequately calibrated using a calibration curve and the Hosmer-Lemeshow test, demonstrating a correlation between the predicted and observed risk. This study presents a novel, validated risk model for preterm birth in pregnant women with GDM, providing an individualized risk estimation using clinical risk factors in the third trimester of pregnancy.

Experimental process characterisation for high-volume compression moulding of hybrid-architecture composites

Compression moulding of woven fabric prepreg and sheet moulding compound (SMC) in a single-shot process combines the superior mechanical properties of continuous fibre composites and the high design flexibility of discontinuous fibre composites. This experimental study presents insights into the moulding characteristics of prepreg and SMC and explores the interaction between these two materials during manufacturing of hybrid-architecture composites. All tests were performed under typical compression moulding conditions using a squeeze flow testing rig. The response of each material was interpreted through proposed material models applied to the data acquired from single architecture tests. Critical interaction mechanisms were identified from hybrid architecture tests, alongside some additional deformation mechanisms, such as high level of in-plane fibre tow deformation not observed in single-architecture tests. Novel experimental methods were also introduced to quantify the change in meso-scale fibre architecture in the prepreg induced by the flow of SMC. The outcomes of this study not only shed light on the essential interaction mechanisms of hybrid-architecture composites but also provide valuable insights into deformation mechanisms not apparent in isolated material studies. Ultimately, these findings will facilitate the future development of a numerical simulation model for hybrid moulding processes.

Multivariable prediction models for fetal macrosomia and large for gestational age: A systematic review.

The identification of large for gestational age (LGA) and macrosomic fetuses is essential for counselling and managing these pregnancies. To systematically review the literature for multivariable prediction models for LGA and macrosomia, assessing the performance, quality and applicability of the included model in clinical practice. MEDLINE, EMBASE and Cochrane Library were searched until June 2022. We included observational and experimental studies reporting the development and/or validation of any multivariable prediction model for fetal macrosomia and/or LGA. We excluded studies that used a single variable or did not evaluate model performance. Data were extracted using the Checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist. The model performance measures discrimination, calibration and validation were extracted. The quality and completion of reporting within each study was assessed by its adherence to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) checklist. The risk of bias and applicability were measured using PROBAST (Prediction model Risk Of Bias Assessment Tool). A total of 8442 citations were identified, with 58 included in the analysis: 32/58 (55.2%) developed, 21/58 (36.2%) developed and internally validated and 2/58 (3.4%) developed and externally validated a model. Only three studies externally validated pre-existing models. Macrosomia and LGA were differentially defined by many studies. In total, 111 multivariable prediction models were developed using 112 different variables. Model discrimination was wide ranging area under the receiver operating characteristics curve (AUROC 0.56-0.96) and few studies reported calibration (11/58, 19.0%). Only 5/58 (8.6%) studies had a low risk of bias. There are currently no multivariable prediction models for macrosomia/LGA that are ready for clinical implementation.

Articular cartilage corefucosylation regulates tissue resilience in osteoarthritis

This study aimed to investigate the glycan structural changes that occur before histological degeneration in osteoarthritis (OA) and to determine the mechanism by which these glycan conformational changes affect cartilage degeneration. An OA model was established in rabbits using mannosidase injection, which reduced high-mannose type N-glycans and led to cartilage degeneration. Further analysis of glycome in human OA cartilage identified specific corefucosylated N-glycan expression patterns. Inhibition of N-glycan corefucosylation in mice resulted in unrecoverable cartilage degeneration, while cartilage-specific blocking of corefucosylation led to accelerated development of aging-associated and instability-induced OA models. We conclude that α1,6 fucosyltransferase is required postnatally to prevent preosteoarthritic deterioration of articular cartilage. These findings provide a novel definition of early OA and identify glyco-phenotypes of OA cartilage, which may distinguish individuals at higher risk of progression.

From isotopically labeled DNA to fluorescently labeled dynamic pili: building a mechanistic model of DNA transport to the cytoplasmic membrane.

SUMMARYNatural competence, the physiological state wherein bacteria produce proteins that mediate extracellular DNA transport into the cytosol and the subsequent recombination of DNA into the genome, is conserved across the bacterial domain. DNA must successfully translocate across formidable permeability barriers during import, including the cell membrane(s) and the cell wall, that are normally impermeable to large DNA polymers. This review will examine the mechanisms underlying DNA transport from the extracellular space to the cytoplasmic membrane. First, the challenges inherent to DNA movement through the cell periphery will be discussed to provide context for DNA transport during natural competence. The following sections will trace the development of a comprehensive model for DNA translocation to the cytoplasmic membrane, highlighting the crucial studies performed over the last century that have contributed to building contemporary DNA import models. Finally, this review will conclude by reflecting on what is still unknown about the process and the possible solutions to overcome these limitations.

Numerical Study of Damage Accumulation in Brittle Spheres During Repeated Impacts and Development of a Conceptual Model for Rocks

Numerical Study of Damage Accumulation in Brittle Spheres During Repeated Impacts and Development of a Conceptual Model for Rocks