Transport Domain Research Articles

RAMPART: Reinforcing Autonomous Multi-agent Protection through Adversarial Resistance in Transportation

In the field of multi-agent autonomous transportation, such as automated payload delivery or highway on-ramp merging, agents routinely exchange knowledge to optimize their shared objective and adapt to environmental novelties through Cooperative Multi-Agent Reinforcement Learning (CMARL) algorithms. This knowledge exchange between agents allows these systems to operate efficiently and adapt to dynamic environments. However, this cooperative learning process is susceptible to adversarial poisoning attacks, as highlighted by contemporary research. Particularly, the poisoning attacks where malicious agents inject deceptive information camouflaged within the differential noise, a pivotal element for differential privacy (DP)-based CMARL algorithms, pose formidable challenges to identify and overcome. The consequences of not addressing this issue are far-reaching, potentially jeopardizing safety-critical operations and the integrity of data privacy in these applications. Existing research has strived to develop anomaly detection-based defense models to counteract conventional poisoning methods. Nonetheless, the recurring necessity for model offloading and retraining with labeled anomalous data undermines their practicality, considering the inherently dynamic nature of the safety-critical autonomous transportation applications. Further, it is imperative to maintain data privacy, ensure high performance, and adapt to environmental changes. Motivated by these challenges, this paper introduces a novel defense mechanism against stealthy adversarial poisoning attacks in the autonomous transportation domain, termed Reinforcing Autonomous Multi-agent Protection through Adversarial Resistance in Transportation (RAMPART). Leveraging a GAN model at each local node, RAMPART effectively filters out malicious advice in an unsupervised manner, whilst generating synthetic samples for each state-action pair to accommodate environmental uncertainties and eliminate the need for labeled training data. Our extensive experimental analysis, conducted in a Private Payload Delivery Network (PPDN) —a common application in the autonomous multi-agent transportation domain—demonstrates that RAMPART successfully defends against a DP-exploited poisoning attack with a \(30\% \) attack ratio, achieving an F1 score of 0.852 and accuracy of \(96.3\% \) in heavy-traffic environments .

Different sedentary behavior domains present distinct associations with eating-related indicators

BackgroundUnhealthful dietary patterns have been consistently associated with low levels of physical activity (PA), but studies dedicated to sedentary behavior (SB) are scarce, especially in adults. The few studies that investigated the association between SB and dietary patterns focused mostly on specific types of SB, such as TV-watching or screen time. SB can be accumulated in distinct domains (i.e., work, transport, and leisure-time), thus, it is key to investigate in depth the impact that different domains of SB can have on eating-related indicators. We aimed to investigate the associations between different SB domains and eating-related indicators, in a sample of adults.MethodsCross-sectional data from students, teachers, and staff from a Portuguese University was collected in November/2021 through an anonymous online survey. Data analyses were performed using the IBM SPSS software (version 28.0) and included descriptive statistics, partial correlations, and group comparisons using one-way ANOVA. Daily average SB at work/study, transport, and in leisure-time were self-reported and eating-related indicators were measured with several items from the Mediterranean Diet Score. Specific eating-related behaviors reflecting a protective eating pattern (e.g., eating breakfast regularly), and eating behavior traits (e.g., external eating) were also assessed. Body mass index (BMI) was calculated as weight (kg)/height(m)2. The International Physical Activity Questionnaire/Short-Form was used to assess PA.ResultsThe sample included 301 adults (60.1% women), with a mean age of 34.5 years. Overall, leisure-time SB was inversely associated with adherence to the Mediterranean diet (r = -0.20; p < 0.001) and with a protective eating profile (r = -0.31; p < 0.001). Higher transport SB was also related to lower adherence to the Mediterranean diet (r = -0.20; p < 0.001) and to an unhealthier eating profile (r = -0.22; p < 0.001), but no associations were found for work-related SB (p > 0.05). These results persisted after the adjustment for BMI, sex, and self-reported PA. These results were impacted by the age tertile.ConclusionsOur findings suggest that adults with higher levels of SB in leisure-time and transport domains tend to report less healthy eating-related behaviors, irrespective of BMI, sex, and PA level. However, some differences in these associations were found according to the age tertile. This information may assist public health authorities in focusing their efforts in augmenting literacy on SB, namely on how SB can be accumulated via different settings. Furthermore, public health literacy efforts need to extend besides the more known deleterious effects of SB on health (e.g., diabetes, cardiovascular disease), to also include the interplay with eating indicators. Strategies to reduce SB and unhealthy eating should be particularly focused on promoting physically active forms of commuting and reducing SB in the leisure setting.

Enhancing flood-response commuting resilience via driving mechanism investigation: New evidence from Wuhan, China

In the context of climate change, transportation resilience to flood threats has received significant attention. However, measures to improve practical resilience have not been addressed and elucidated. Various studies from the domains of disaster and transport have been devoted to commuting vulnerability analysis and transport network robustness assessment, whereas a systematic perspective is rarely used to analyze the damage mechanism of floods to the commuting system. To fill this research gap, we constructed an index system from the dimensions of hazard, transport network condition, and commuting pattern as driving factors of commuting risk during floods. The contribution to commuting loss was first assessed to guide intervention prioritization based on flood modeling and commute simulation. We adopted gradient boosting decision trees to examine the relationship between driving factors from three dimensions and commuting. The results of this study conducted in Wuhan indicate that the contribution rate of commuting pattern is the highest, followed by hazard, and finally road network conditions. The effective thresholds of the driving factors were also identified to guide urban governance. This study is a novel exploration of disaster impact mechanism that can provide new insights for the improvement of commuting resilience practices under climate change.

Rlm-tracking: online multi-pedestrian tracking supported by relative location mapping

The challenge of multi-object tracking stands as a fundamental focus in computer vision research, finding widespread applications in areas such as public safety, transportation, autonomous vehicles, robotics, and other domains involving artificial intelligence. Given the intricate nature of natural scenes, the occurrence of object occlusion and semi-occlusion is commonplace in basic tracking tasks. These factors often result in challenges such as ID switching, object loss, detection errors, and misaligned bounding boxes, thereby significantly impacting the precision of multi-object tracking.This paper aims to address the aforementioned issues and proposes a novel multi-object tracker, incorporating Relative location mapping (RLM) and Target region density (TRD) modeling. The new tracker is more sensitive to differences in the spatial relationships between targets, allowing it to dynamically introduce low-scoring detection boxes into different regions based on the density of target regions in the image. This improves the accuracy of target tracking while avoiding the consumption of a significant amount of computational resources.Our research results indicate that when applying this method to state-of-the-art multi-object tracking approaches, the proposed model achieves improvements of 0.4 to 0.8 points in the HOTA and IDF1 metrics on the MOT17 and MOT20 datasets. This demonstrates the effectiveness of the proposed method in enhancing multi-object tracking performance.

Deep learning and multi-modal fusion for real-time multi-object tracking: Algorithms, challenges, datasets, and comparative study

Real-time multi-object tracking (MOT) is a complex task involving detecting and tracking multiple objects. After the objects are detected, they are assigned markers, and their trajectories are tracked in real-time. The scientific community is intrigued by the possibilities of utilizing MOT technology in the context of smart cities. Their primary focus lies in the domains of intelligent transportation, detection of vehicles and pedestrians, crowd surveillance, and public safety. Deep learning techniques have been developed in recent years to effectively tackle the challenges of real-time MOT tasks and enhance tracking performance. Environmental perception within smart traffic applications relies heavily on sensor data fusion. In traffic scenarios, a thoughtful approach involves utilizing a combination of sensors and cameras to detect and track targets while gathering valuable data effectively. However, it faces challenges when it comes to detecting and tracking objects that are in motion, have complex changes in appearance, or are in crowded scenes. This paper explores the foundational standard for real-time Multiple Object Tracking tasks. We prioritize the examination of quantitative measures by conducting a comprehensive analysis of widely utilized benchmark datasets and metrics. This study also investigates established embedding techniques and multi-modal fusion methods within real-time multi-target tracking algorithms. Each strategy will be classified and assessed according to a predefined set of principles. The paper presents a comprehensive analysis and visual representation of various MOT strategies. Finally, this paper aims to present an overview of the current challenges faced by the MOT mission, as well as the potential objectives that lie ahead.

Data Sources and Models for Integrated Mobility and Transport Solutions.

The number of data sources and models in the mobility and transport domain strongly proliferated in the last decade. Most formats have been created to enable specific and innovative applications. On the other hand, the available data models present a certain degree of complexity in terms of their integration and management due to partial overlaps, and in most cases, they could be exploited alternatively to implement the same smart and latest innovative solutions. This paper offers an overview of data models, standards and their relationships. A second contribution highlights any possible exploitation of data models for implementing operational processes for city transportation management and for the feeding of simulation and optimization processes that produce other data results in other data models. The final goal in most cases is the monitoring and control of city transport conditions, as well as the tactic and strategic planning of city infrastructure. This work was developed in the context of the CN MOST, a national center of sustainable mobility in Italy, and it is based on exploiting the Snap4City platform.

Impacts of the first wave of the COVID-19 pandemic on leisure and transportation physical activity among healthcare workers

IntroductionThe COVID-19 pandemic may lead to reduced physical activity (PA) in health care workers (HCWs). ObjectiveTo evaluate leisure and transport-related PA in HCW of a COVID-19-dedicated hospital during the first wave of the COVID-19 pandemic. MethodsThis is a cross-sectional study with a sample of 1,527 HCWs. Socioeconomic aspects, occupational characteristics, and engagement in leisure and transport-related PA were investigated through an online survey administered in August of 2020. ResultsMore than 80 % HCWs performed < 150 min/week of leisure-related PA, and 85 % performed ≤ 30 min/day transport-related PA. Being male was associated with more PA (OR: 1.93; 95 % CI:1.40–2.66) and transport-related PA; working in nursing, physical therapy, and cleaning/housekeeping services was associated with low PA (OR: 0.70; 95 % CI:0.51–0.95). Physicians and administrative staff were less active in transport-related PA. ConclusionsHCWs working in a COVID-19 hospital had low levels of PA in the domains of leisure and transportation.

Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality.

Plant virus genomes encode proteins that are involved in replication, encapsidation, cell-to-cell, and long-distance movement, avoidance of host detection, counter-defense, and transmission from host to host, among other functions. Even though the multifunctionality of plant viral proteins is well documented, contemporary functional repertoires of individual proteins are incomplete. However, these can be enhanced by modeling tools. Here, predictive modeling of proteins encoded by the two genomic RNAs, i.e., RNA1 and RNA2, of grapevine fanleaf virus (GFLV) and their satellite RNAs by a suite of protein prediction software confirmed not only previously validated functions (suppressor of RNA silencing [VSR], viral genome-linked protein [VPg], protease [Pro], symptom determinant [Sd], homing protein [HP], movement protein [MP], coat protein [CP], and transmission determinant [Td]) and previously identified putative functions (helicase [Hel] and RNA-dependent RNA polymerase [Pol]), but also predicted novel functions with varying levels of confidence. These include a T3/T7-like RNA polymerase domain for protein 1AVSR, a short-chain reductase for protein 1BHel/VSR, a parathyroid hormone family domain for protein 1EPol/Sd, overlapping domains of unknown function and an ABC transporter domain for protein 2BMP, and DNA topoisomerase domains, transcription factor FBXO25 domain, or DNA Pol subunit cdc27 domain for the satellite RNA protein. Structural predictions for proteins 2AHP/Sd, 2BMP, and 3A? had low confidence, while predictions for proteins 1AVSR, 1BHel*/VSR, 1CVPg, 1DPro, 1EPol*/Sd, and 2CCP/Td retained higher confidence in at least one prediction. This research provided new insights into the structure and functions of GFLV proteins and their satellite protein. Future work is needed to validate these findings.

Social cost-benefit analysis of different types of buses for sustainable public transportation

The transportation sector holds significant importance within a nation, constituting a considerable share of its total energy consumption. In developing nations, the predominant use of non-renewable energy sources like natural gas and petroleum-based fuels is notably observed in the transportation domain. Both public and private vehicles predominantly operate on fossil fuels, giving rise to concerns such as depleting national energy resources and escalating environmental impacts. Consequently, addressing these challenges calls for a shift towards cleaner and more sustainable transportation options. Therefore, the purpose of this research is to conduct a comprehensive ex-ante social cost-benefit analysis of various types of buses for public transportation. Specifically, the types of buses considered include: (i) solar buses, (ii) electric buses, (iii) hydrogen buses, and (iv) diesel buses. This study investigates the feasibility of each type of bus by determining its overall benefits and costs. The economic, environmental, and social impacts are determined and monetized which are used to calculate the net present values (NPV). The solar bus was found to have a comparatively higher NPV of 79.46 Million PKR which demonstrates that the associated overall benefits are higher as compared to the costs. On the contrary, the hydrogen bus was found to have a comparatively lower NPV of −5.87 Million PKR which depicted that considerably higher costs surpassed its benefits. Finally, an exhaustive sensitivity analysis was performed to investigate the impacts of critical system parameters on the feasibility of the examined buses.

Cell chip device for real-time monitoring of drug release from drug-laden microparticles.

A cell chip is a microfluidic cell culture device fabricated using microchip manufacturing methods for culturing living cells in a micrometer-sized chamber to model the physiological functions of tissues and organs. It has been extensively investigated in the domain of drug transport and toxicity research. Herein, we developed a cell chip for real-time monitoring of drug release from drug carriers. The proposed system integrates three core functions: cell culture, real-time analysis, and drug delivery tests. This device was designed to be loaded with microparticles for drug release and to enable real-time drug measurement. The efficacy of the developed system was evaluated by measuring the concentration of drugs released from the microparticles prepared with poly(lactic-co-glycolic acid) (PLGA). Doxorubicin, an anticancer drug, was used as a model drug and A549 cells, a type of lung cancer cell, were simultaneously cultured to compare the drug release concentrations in the presence of cells. Furthermore, variations in cell viability with respect to the presence of drug-loaded microparticles were observed and analyzed. Notably, as the proposed system requires an extremely small number of microparticles, it affords simple implementation in a single device, thereby eliminating the need for complex accessories and instruments for analysis. Thus, the analysis process becomes more convenient and cost-efficient. Thus, the proposed method offers an easy analysis of the release behavior of various cells and drugs. The simplicity and low cost of this innovative system without sacrificing analytical precision demonstrate its potential for applications across various fields.

ProteoMutaMetrics: machine learning approaches for solute carrier family 6 mutation pathogenicity prediction.

The solute carrier transporter family 6 (SLC6) is of key interest for their critical role in the transport of small amino acids or amino acid-like molecules. Their dysfunction is strongly associated with human diseases such as including schizophrenia, depression, and Parkinson's disease. Linking single point mutations to disease may support insights into the structure-function relationship of these transporters. This work aimed to develop a computational model for predicting the potential pathogenic effect of single point mutations in the SLC6 family. Missense mutation data was retrieved from UniProt, LitVar, and ClinVar, covering multiple protein-coding transcripts. As encoding approach, amino acid descriptors were used to calculate the average sequence properties for both original and mutated sequences. In addition to the full-sequence calculation, the sequences were cut into twelve domains. The domains are defined according to the transmembrane domains of the SLC6 transporters to analyse the regions' contributions to the pathogenicity prediction. Subsequently, several classification models, namely Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) with the hyperparameters optimized through grid search were built. For estimation of model performance, repeated stratified k-fold cross-validation was used. The accuracy values of the generated models are in the range of 0.72 to 0.80. Analysis of feature importance indicates that mutations in distinct regions of SLC6 transporters are associated with an increased risk for pathogenicity. When applying the model on an independent validation set, the performance in accuracy dropped to averagely 0.6 with high precision but low sensitivity scores.

Leisure-time physical activity as a key protective factor against cognitive decline in older adults: an isotemporal substitution analysis.

This study aimed to test hypothesized effects of replacing sedentary behavior with moderate-to-vigorous physical activity, sleep, and different domains of physical activity by equivalent amounts on suggestive cognitive decline in an older adult population. This was a cross-sectional study including 473 older adults aged ≥ 60 years. Cognitive decline was assessed using the Mini-Mental Health Examination. Physical activity, its different domains and the time of exposure to sedentary behavior were assessed using the International Physical Activity Questionnaire. For data analysis, two isotemporal substitution models were constructed using Poisson regression. The first model tested the effect of sleep time, sedentary behavior, and moderate-to-vigorous physical activity on cognitive decline. The second model was used to determine the effect of physical activity domains (leisure, work, transport, and home), sleep time, and sedentary behavior on cognitive decline. Physical activity during leisure time was protective against cognitive decline among all domains tested, replacing sedentary behavior, sleep, and transport. Conversely, substitution of the leisure domain for sedentary behavior, sleep, and transport was considered a risk factor for cognitive decline. Leisure time proved to be a strong protective factor in reducing the risk of cognitive decline, and it is necessary to encourage and stimulate public policies that include it.

Comparison of fuzzy MCDM approaches in the elevator selection problem

Considering a part of the problem in the domain of vertical transport, and above all elevators, it is noticeable that there are a large number of technically feasible alternatives in most cases. The designer's task is to choose from a set of possible solutions that best match the technical and economic conditions defined by the project task. The design and appropriate MCDM technique determine the quality of the recommended decision, saving computational time without sacrificing quality in the final ranking of alternatives. The task is to choose solutions that best match the technical and economic conditions. Therefore, selecting an adequate elevator is a problem that requires the manipulation of a large number of different data and, at the same time, the inclusion of a significant number of relevant criteria and goals that can often conflict. Fuzzy logic has proven excellent in models where intuition and judgment are primary elements. This paper aims to present a systematic overview of the developed fuzzy MCDM techniques. By ranking and comparative analysis of the results, this research represents an attempt to choose a method to support decision-making and create a comprehensive tool in the elevator selection process.

Giant manipulation of thermal conductivity anisotropy in black phosphorene under external electric fields.

The thermal anisotropy of materials holds significant theoretical and practical implications in the domains of thermal transport and thermoelectricity. Black phosphorene, a novel two-dimensional (2D) semiconductor, is notable for its exceptional chemical and physical properties, attracting substantial attention for its thermal transport characteristics. Similar to other 2D materials, black phosphorene exhibits pronounced in-plane thermal anisotropy. Given its expanding applications in nanoelectronics, optoelectronics, and thermoelectrics, there is a growing need to manipulate its anisotropic thermal transport. Current methods for adjusting anisotropy or isotropy typically involve structural engineering or materials processing, which are often costly, time-consuming, and irreversible. In contrast, little progress has been made with methods that are intact, robust, and reversible. Driven by the intrinsic relationship between interatomic interaction-mediated phonon transport and electronic charges, we conduct a comprehensive investigation into the impact of an external electric field on the thermal transport properties of 2D black phosphorene using first-principles calculations and the phonon Boltzmann transport equation. Our findings reveal that applying an electric field in the Zigzag direction reduces the lattice thermal conductivity of black phosphorene, with the Zigzag direction being more responsive to the electric field than the Armchair direction. By adjusting the electric field to a maximum of E(f_xx) = 0.2 V Å-1, the anisotropic thermal conductivity of black phosphorene decreases by more than 28%, demonstrating effective manipulation of anisotropy. This significant transition in anisotropic thermal transport arises from the substantial reduction in thermal conductivity along the Zigzag direction at moderate electric field strengths. The underlying cause of this variation in anisotropy can be attributed to changes in group velocity, with the phonon lifetime serving as a scaling factor for reducing anisotropy. Analysis of the electronic structures shows that stronger electric fields induce more charges, enhancing the screening effect. The electric field significantly alters thermal conductivity by affecting bond ionicity and anharmonicity. Our study introduces a robust approach for tuning the anisotropy of phonon transport in materials using an external electric field, without altering the atomic structure, thus offering considerable advantages for applications in nanoelectronics and thermoelectric energy conversion.

Sequence-dependent self-assembly of supramolecular nanofibers in periodic dynamic block copolymers

Periodic dynamic block copolymers self-assemble into high aspect ratio nanofibers with well-ordered PEG and PDMS domains for ion transport.

Study of the dynamic process in a nonlinear mathematical model of the transverse oscillations of a moving beam under perturbed boundary conditions

The study of transverse oscillations of systems moving along their axis is a very difficult, but at the same time a very important task. Mathematical models of nonlinear transverse oscillations of a beam moving along its axis are analyzed in this paper work, both for non-resonant and resonant cases. The task becomes even more complicated if we additionally take into account the method of fastening the ends of the beam or the perturbation at its ends. We have obtained dependencies that can be used in construction, transport, industry, mechanical engineering and other domains of technology, ensuring the stability and safety of the operation of such mechanical systems. Mathematical models have been obtained for structural engineers to determine the amplitude–frequency response of relevant structures. These mathematical models are key to researching the dynamics of moving media. The obtained results allow considering not only the influence of kinematic and physical-mechanical parameters on the amplitude–amplitude frequency response of the medium, but also the fastening method. In addition, the correlations obtained in the paper make it possible to study not only the influence of the moving medium parameters on the nature of changes in the frequency and amplitude of oscillations, but also to consider the movement at the points of support of the medium. Namely, even at the stage of designing a pipeline for a liquid flowing at a certain speed, it is possible to consider the influence of the oscillation of the supports or their fastening method on the dynamics of the oscillatory process. The resulting dependencies allow designers to consider the influence of the characteristics given in the paper with a high level of accuracy and predict dynamic phenomena in them. In engineering calculations of various mechanical systems, the resulting dependencies can be used to optimize parameters to avoid negative destructive phenomena during operation.

Association between physical activity domains and depressive symptoms among Brazilian adults: does every move count?

This study aimed to investigate the practice of physical activities in the four domains (leisure time, transportation, household, and work) and the prevalence of depressive symptoms in Brazilian adults, in general and stratified by sex, schooling level, and having or not a self-reported diagnosis of depression. This is a cross-sectional study with data from 88,531 individuals aged 18 years or older, who responded to the Brazilian National Health Survey in 2019. The depressive symptoms were evaluated by the Patient Health Questionnaire-9 (PHQ-9). Those who practice physical activities at least once a week in a given domain were considered physically active. Additionally, the calculation of physical activities duration was conducted and later divided into quartiles for each domain. For the association analyses, the crude odds ratio (crudeOR) and adjusted odds ratio (adjustedOR) were calculated for the total and stratified analyses. Individuals who are physically active during leisure time showed a lower chance of presenting depressive symptoms, in total (adjustedOR = 0.74; 95%CI: 0.64-0.86) and in all stratifications, except for individuals with self-reported depression. The associations of leisure-time physical activity were most frequent in those who practice from 121 to 360 minutes/week. The individuals who were active in the transportation, household, and work domains had a higher chance of presenting depressive symptoms in some groups, with more consistent results for household physical activities. The results showed that the relationship between physical activities and depression among Brazilians varies according to domain and duration, and that the concept that "every move counts" seemed to be correct only for the leisure-time domain.

Associação entre domínios da atividade física e sintomas depressivos em adultos brasileiros: todo movimento conta?

Abstract: This study aimed to investigate the practice of physical activities in the four domains (leisure time, transportation, household, and work) and the prevalence of depressive symptoms in Brazilian adults, in general and stratified by sex, schooling level, and having or not a self-reported diagnosis of depression. This is a cross-sectional study with data from 88,531 individuals aged 18 years or older, who responded to the Brazilian National Health Survey in 2019. The depressive symptoms were evaluated by the Patient Health Questionnaire-9 (PHQ-9). Those who practice physical activities at least once a week in a given domain were considered physically active. Additionally, the calculation of physical activities duration was conducted and later divided into quartiles for each domain. For the association analyses, the crude odds ratio (crudeOR) and adjusted odds ratio (adjustedOR) were calculated for the total and stratified analyses. Individuals who are physically active during leisure time showed a lower chance of presenting depressive symptoms, in total (adjustedOR = 0.74; 95%CI: 0.64-0.86) and in all stratifications, except for individuals with self-reported depression. The associations of leisure-time physical activity were most frequent in those who practice from 121 to 360 minutes/week. The individuals who were active in the transportation, household, and work domains had a higher chance of presenting depressive symptoms in some groups, with more consistent results for household physical activities. The results showed that the relationship between physical activities and depression among Brazilians varies according to domain and duration, and that the concept that “every move counts” seemed to be correct only for the leisure-time domain.

The ceramide transport protein CERT is involved in alkylacylglycerol transfer from the ER to the Golgi for the biosynthesis of ether phospholipid

Ether phospholipids are synthesized by a series of enzymes localized in peroxisomes, the endoplasmic reticulum (ER), and the Golgi apparatus. During this process, the lipid intermediate alkylacylglycerol (AAG) synthesized in the ER is transferred from the site of its synthesis to the Golgi apparatus. In this study, we determined whether ceramide transport protein (CERT) is a candidate for AAG transfer. A lipid transfer assay revealed that CERT can mediate AAG transfer between phospholipid liposomes. AAG transport activity was markedly inhibited by the CERT inhibitor HPA-12 and reduced when the lipid transport domain of CERT was deleted. Suppression of CERT in HEK293 cells resulted in increased levels of plasmanyl-PC, which is synthesized by the ER-residing choline/ethanolamine phosphotransferase 1 (CEPT1). The mRNA levels and enzymatic activity of plasmanyl-PC synthesizing enzymes were not increased in CERT-deficient cells, indicating that the increase in plasmanyl-PC results from AAG accumulation in the ER. Re-introduction of CERT into CERT-deficient cells caused a decrease in plasmanyl-PC. Taken together, our findings suggest for the first time that CERT is involved in the transfer of AAG from the ER to the Golgi apparatus and plays a role in the biosynthesis of ether phospholipids.

Flexible open network operating system architecture for implementing higher scalability using disaggregated software‐defined optical networking

AbstractThe enhanced capacity of optical networks is a significant advantage within the global telecommunications industry. Optical networks provides transmission of information over large distances with reduced latency. However, the growing intricacy of network topologies poses a significant challenge to network adaptability, network resilience, device compatibility, and service quality in the contemporary era of technology and 5G networks. In light of these challenges, recent studies leverages on disaggregation in the context of Software Defined Network (SDN) and network service orchestrators as a viable remedy. Disaggregated optical systems offer SDON (Software‐Defined Optical Networking) enhanced control options and third‐party dynamism streamlining upgrades and diminishing single vendor dependency. Although, the advancement of disaggregation improves network flexibility and vendor neutrality of Software Defined Optical Networking (SDON), this improvement comes at the cost of reduced scalability and network controllability performance. The current research paper posits two potential resolutions to the aforementioned challenge. The authors present recommendations and an enhanced architecture that leverages Open Network Operating System (ONOS) containers and Kubernetes orchestration to improve scalability inside the Software‐Defined Optical Networking (SDON) architecture. The suggested architectural design has underlining novel flow charts and algorithms that enhances scalability performance by 33% while also preserving flexibility and controllability in comparison to pre‐existing SDON architectures. This architecture also makes use of the Mininet‐Optical physical‐layer architecture to simulate a real‐time scenario, as well as yang models from the Open Disaggregated Transport Network (ODTN) working group, the pioneers of SDONs. A detailed analysis of the rules and procedural processes involved in the implementation of the proposed architecture. In order to demonstrate the practical application of this architectural framework to a real‐world Software‐Defined Optical Network (SDON) system, the pre‐existing SDON ONOS architecture within the Optical Transport Domain Networking (OTDN) working group was adjusted and refined. This adaptation aimed to illustrate the use of ONOS in conjunction with established optical network systems, highlighting the advantages it offers.