Propagation Method Research Articles

Particle Propagation and Electron Transport in Gases

In this review, we detail the commonality of mathematical intuitions that underlie three numerical methods used for the quantitative description of electron swarms propagating in a gas under the effect of externally applied electric and/or magnetic fields. These methods can be linked to the integral transport equation, following a common thread much better known in the theory of neutron transport than in the theory of electron transport. First, we discuss the exact solution of the electron transport problem using Monte Carlo (MC) simulations. In reality we will go even further, showing the interpretative role that the diagrams used in quantum theory and quantum field theory can play in the development of MC. Then, we present two methods, the Monte Carlo Flux and the Propagator method, which have been developed at this moment. The first one is based on a modified MC method, while the second shows the advantage of explicitly applying the mathematical idea of propagator to the transport problem.

In vitro propagation of stone fruit rootstock cultivar Evrica 99 and its influence on some phytochemical traits of fresh apricot fruit

Background: The cultivation of stone fruits is of primary importance in Armenia. Their fruits contain antioxidants, fiber, potassium, vitamin A, C, E, minerals, etc., which have a beneficial effect on human health and prevent many diseases. The concentration of those components varies depending on ecological factors, cultivar, rootstock, cultural practices, etc. Clonal rootstocks are important for increasing orchard density, tree uniformity, and high yields, and they can also affect fruit quality. In vitro culture is a valuable method for rapid propagation of high-quality, virus free plant material. Objective: The purpose of this study was to develop in vitro production technology for the stone-fruit rootstock cultivar Evrica 99, and to determine if rootstocks affect some fresh fruit traits of ´Yerevani´ and ´Sateni´ apricot cultivars. Methods: The shoot apical meristem and lateral bud served as explants for shoot regeneration. Different sterilizing agents at various periods of exposure were used for the explant surface sterilization. Various concentrations of phytohormones, both individually and in combinations, were employed for in vitro regeneration and rooting of plants. The titratable acidity (TA), dry matter (DM), vitamin C, mineral content, total carotenoids (TC), and sugar contents were evaluated in fresh fruit. Results: The most optimal option for explant surface sterilization was the gradual application of calcium hypochlorite [Ca (ClO)2] (2.0% solution, exposure time 10 min) and ethanol (70% solution, exposure time 20 s), as a result of which we had 75.5% survival rate of explants. The efficient medium for in vitro shoot regeneration was MS supplemented with 6-Benzylaminopurine (BAP) 0.8 mg/l, Kinetin (Kin) 0.2 mg/l, and Gibberellic acid (GA3) 1.0 mg/l. The half-strength Murashige and Skoog (MS) medium containing 0.8 mg/l indole-3-butyric acid (IBA) was optimal for in vitro rooting. Rooted plants were successfully adapted with a survival rate of 85.0%. The defined method can be successfully used for 'Evrica 99' cultivar micropropagation. The results obtained showed that fruit quality strongly depended on both the varieties and the rootstock tested. Conclusion: In the current study, an alternative in vitro propagation technology for rootstock cultivar ´ Evrica 99 was developed by direct organogenesis, enabling mass-scale production of virus-free plants that is suitable for commercial purposes as well. The apricot cultivars ´Yerevani´ and ´Sateni´ grafted on the virus-free rootstock cultivar Evrica 99 showed higher fruit quality traits, which are essential for human health and diet. Keywords: ´Evrica 99, fucnctional foods, in vitro regeneration, micropropagation, plant growth regulators, stone fruit rootstock, tissue culture.

A Review on Plant Tissue Culture

Plant tissue and cell culture involve the aseptic cultivation and propagation of plant cells, tissues, and organs in a meticulously controlled laboratory setting. This innovative method harnesses the potential of nutrient-rich media to efficiently replicate plant cells on a large scale, resulting in the rapid production of mature and disease-free plants. The cornerstone of commercial technology in this field is micropropagation, a process that achieves swift proliferation from minute plant cuttings, axillary buds, and, to a limited extent, from somatic embryos and cell clumps in suspension cultures and bioreactors. Micropropagation, a pivotal aspect of plant tissue and cell culture, holds immense value in generating high-quality and consistent planting materials. These materials find applications across diverse fields, including molecular genetic engineering, plant breeding, horticulture production, and environmental preservation. The process of micropopagation unfolds through several distinct stages, including propagation, subculture of explants for proliferation, shooting and rooting, and hardening. These stages collectively form a universal framework for large-scale multiplication of plants. This technique plays a critical role in overcoming the limitations of traditional plant propagation methods. By allowing for controlled and accelerated growth in a laboratory environment, plant tissue and cell culture contribute to the efficient production of disease-resistant and genetically uniform plant materials. This not only supports advancements in various scientific disciplines but also addresses practical needs in agriculture, horticulture, and environmental conservation. The continuous refinement and application of plant tissue and cell culture methods underscore their significance in meeting the growing demands for sustainable and high-quality plant materials across diverse sectors.

Synthesis and Accumulation of Phytocompounds in Field-, Tissue-Culture Grown (Stress) Root Tissues and Simultaneous Defense Response Activity in Glycyrrhiza glabra L.

Harsh climates, i.e., drought, extreme temperatures, and toxic gases, pose issues to agriculture by altering plants’ growth and yield. Biotechnology with biochemical defense approaches is beneficial for generating new plants/varieties with extra resilience to adverse conditions. In response to stress, cultures show an enriched level of secondary metabolite synthesis. Here, an efficient in vitro propagation method using axillary shoot proliferation, along with callus formation, was established in Glycyrrhiza glabra L. The phytochemical composition of in vitro and in vivo grown tissues was analyzed using a gas chromatography–mass spectrometry (GC–MS) technique, and the biochemical attributes were measured and compared in different investigated tissues. Callus formation from root explants was achieved with a frequency of 88.89% on MS medium containing 2.0 mg/L BAP and 0.5 mg/L 2,4-D. Axillary shoot proliferation was obtained from dormant buds when cultured onto MS supplemented with BAP alone, or in combination with, IAA. The maximum shoot proliferation (94.44%) was recorded on MS with 1.0 mg/L BAP with an average shoot length of 10.5 cm. The regenerated shoots were subcultured and transferred to the root induction medium, supplemented with various concentrations of IAA/IBA, wherein 2.0 mg/L IBA resulted in the best rooting frequency (88.89%). The GC–MS-based phytocompounds analysis of the methanolic extracts of root-derived callus and in vivo- and in vitro- grown root tissues was conducted. These samples revealed the presence of more than 35 therapeutically important bioactive compounds, such as methylglabridin, sitosterol, lupeol, squalene, stearic acid, linoleic acid, etc. The biochemical parameters, like total phenolic content, flavonoid content, DPPH scavenging activity, superoxide dismutase, and peroxidase activity were also measured. All the biochemical attributes were found to be higher in in vitro derived roots than the callus and in vivo grown root (donor) samples. These findings demonstrated that callus (root derived) and in vitro roots are a stable and potent source of multiple phytocompounds, encompassing medical significance with wide applications. This study may serve as an alternative opportunity in the sustained and continuous synthesis of important compounds without harming natural vegetation and normal environment.

HITS-based Propagation Paradigm for Graph Neural Networks

In this article, we present a new propagation paradigm based on the principle of Hyperlink-Induced Topic Search (HITS) algorithm. The HITS algorithm utilizes the concept of a “self-reinforcing” relationship of authority-hub. Using HITS, the centrality of nodes is determined via repeated updates of authority-hub scores that converge to a stationary distribution. Unlike PageRank-based propagation methods, which rely solely on the idea of authorities (in-links), HITS considers the relevance of both authorities (in-links) and hubs (out-links), thereby allowing for a more informative graph learning process. To segregate node prediction and propagation, we use a Multilayer Perceptron in combination with a HITS-based propagation approach and propose two models: HITS-GNN and HITS-GNN+. We provided additional validation of our models’ efficacy by performing an ablation study to assess the performance of authority-hub in independent models. Moreover, the effect of the main hyper-parameters and normalization is also analyzed to uncover how these techniques influence the performance of our models. Extensive experimental results indicate that the proposed approach significantly improves baseline methods on the graph (citation network) benchmark datasets by a decent margin for semi-supervised node classification, which can aid in predicting the categories (labels) of scientific articles not exclusively based on their content but also based on the type of articles they cite.

An efficient nonparaxial and bidirectional wave propagation method for optical fibers & fiber components

We present an efficient method for nonparaxial (i.e., wide angle) and bidirectional wave propagation in optical fibers and fiber components. The method is applicable to all structures such as tapers and fiber gratings which are azimuthally symmetric in respect of the refractive index distribution and the geometry. We use the collocation method to numerically solve the Helmholtz equation as this method allows to take advantage of the symmetry of the structure. This results in reduction of a 3-D propagation problem to a 2-D propagation problem giving a computational advantage by orders of magnitude. The method can also be used to obtain modes with similar computational advantage. The method works for fields of all azimuthal symmetries, i.e., one can treat all LPlm modes. In order to show the applicability of the method we have included examples on obtaining modes (LP and OAM) in step-index and ring core fibers (RCFs), propagation through a tapered RCF and computation of reflection spectra of few-mode fiber Bragg gratings (FM-FBGs).

Enhancing the understanding of bolt loosening and wave transmission in bolted lap-joint connections: a numerical and experimental study using guided Lamb waves

Bolt loosening in bolted lap-joint connections can result in catastrophic failures in industrial settings. Therefore, it is crucial to investigate these connections thoroughly and implement preventative measures. However, examining such connections requires considering multiple influencing parameters. In this study, numerical simulations were conducted using three-dimensional finite element analysis in Abaqus software to evaluate these parameters in detail. The study employed a guided wave-based structural health monitoring approach and a precise ultrasonic-guided Lamb wave propagation method. Time signals were analyzed using phase shift and transmission coefficient (TC) factors as suitable indicators. Additionally, the S0 mode of the captured waves provided a highly informative wave packet for analysis due to its specific order. Initially, various simulations were carried out to determine the optimal length of the overlapping region that would result in the most effective transmission of waves. Increasing the bolt torque and the contact friction coefficient between the two plates in the overlapping region reduces the time for waves to reach the sensor and increases the TC of the passing waves. However, the saturation state does not cause any change in the received signals. Increasing the bolt torque is more effective in facilitating wave transmission through the connections than increasing the contact friction coefficient. Furthermore, variations in excitation voltage do not affect the arrival time or TC of the waves. The continuous wavelet transformation enabled a more detailed time-frequency analysis. The results demonstrate that changes in bolt torque do not impact the wave frequencies reaching the sensors, making the frequency insensitive to bolt loosening. Ultimately, the numerical simulation results were validated through experiments on a table-top laboratory sample. This research enhances researchers’ understanding of monitoring the health status of bolted lap-joint connections by investigating the impact of various parameters on guided Lamb wave transmission through these connections.

A second‐order time discretizing block‐centered finite difference method for compressible wormhole propagation

AbstractIn this paper, a second‐order time discretizing block‐centered finite difference method is introduced to solve the compressible wormhole propagation. The optimal second‐order error estimates for the porosity, pressure, velocity, concentration and its flux are established carefully in different discrete norms on non‐uniform grids. Then by introducing Lagrange multiplier, a novel bound‐preserving scheme for concentration is constructed. Finally, numerical experiments are carried out to demonstrate the correctness of theoretical analysis and capability for simulations of compressible wormhole propagation.

Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy storage batteries

At present, the accurate establishment of the battery model and the effective state of health (SOH) estimation under actual energy storage conditions have become the main problems in new energy storage stations. Therefore, a SOH estimation method based on cooperative competitive particle swarm optimization (CCPSO) and nonlinear coefficient temperature decreasing simulated annealing-back propagation (NSA-BP) is proposed. The novelty of this research mainly includes the design of extraction methods in different health indicators (HIs) and the construction of developed NSA-BP network for SOH estimation. In this research, the contributions of SOH estimation are mainly to assist in battery replacement and provide relevant economic reference. Low-rate constant current energy storage degradation experiments and a variable-rate energy storage degradation experiment are performed for different battery packs at 25 °C. The experimental results indicate that the root mean square error (RMSE) and the mean absolute error (MAE) of the proposed method are 0.00588 and 0.00481 under the 0.5 rate condition, and the corresponding values are 0.00732 and 0.00639 under the variable-rate condition. Under the same condition, the proposed SOH estimation method is superior to the methods before improvement in RMSE and MAE, which can provide a basis for efficient monitoring of energy storage batteries.

Investigations of Flow Boiling in Mini-Channels: Heat Transfer Calculations with Temperature Uncertainty Analyses

The article aims to explore boiling heat transfer in mini-channels with a rectangular cross-section using various fluids (HFE-649, HFE-7000, HFE-7100, and HFE-7200). Temperature measurements were conducted using infrared thermography for the heated wall and K-type thermocouples for the working fluid. The 2D mathematical model for heat transfer in the test section was proposed. Local heat transfer coefficients between the heated wall and the working fluid were determined from the Robin condition. The problem was solved by means of the finite element method (FEM) with Trefftz functions. The values of the heat transfer coefficient that were obtained were compared with the results calculated from Newton’s law of cooling. The average relative differences between the obtained results did not exceed 4%. The study included uncertainty analyses for temperature measurements with K- and T-type thermocouples. Expanded uncertainties were calculated using the uncertainty propagation and Monte Carlo methods. Precisely determining the uncertainties in contact temperature measurements is crucial to ensure accurate temperature data for subsequent heat transfer calculations. The results of the heat transfer investigations were compared in terms of fluid temperature, heat transfer coefficients, and boiling curves. HFE-7200 consistently exhibited the highest fluid temperature and temperature differences at boiling incipience, while HFE-7000 demonstrated the highest heat transfer coefficients. HFE-649 showed the lowest heat transfer coefficients. The boiling curves exhibited a typical shape, with a notable occurrence of ‘nucleation hysteresis phenomena’. Upon the analysis of two-phase flow patterns, bubbly and bubbly-slug structures were observed.

Enhancing Semi Supervised Semantic Segmentation Through Cycle-Consistent Label Propagation in Video

To perform semantic image segmentation using deep learning models, a significant quantity of data and meticulous manual annotation is necessary (Mani in: Research anthology on improving medical imaging techniques for analysis and intervention. IGI Global, pp. 107–125, 2023), and the process consumes a lot of resources, including time and money. To resolve such issues, we introduce a unique label propagation method (Qin et al. in IEEE/CAA J Autom Sinica 10(5):1192–1208, 2023) that utilizes cycle consistency across time to propagate labels over longer time horizons with higher accuracy. Additionally, we acknowledge that dense pixel annotation is a noisy process (Das et al. in: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 5978–5987, 2023), whether performed manually or automatically. To address this, we present a principled approach that accounts for label uncertainty when training with labels from multiple noisy labeling processes. We introduce two new approaches; Warp-Refine Propagation and Uncertainty-Aware Training, for improving label propagation and handling noisy labels, respectively, and support the process with quantitative and qualitative evaluations and theoretical justification. Our contributions are validated on the Cityscapes and ApolloScape datasets, where we achieve encouraging results. In later endeavors, the aim should be to expand such approaches to include other noisy augmentation processes like image-based rendering methods (Laraqui et al. in Int J Comput Aid Eng Technol 18(5):141–151, 2023), thanks to the noisy label learning approach.

In Vitro Propagation Journey of Ornamental Gladiolus (Gladiolus Species): A Systematic Review Analysis Based on More Than 50 Years Research

Traditional gladiolus propagation methods are now supplemented with in vitro propagation to meet the demands of modern floriculture in terms of quick production of disease-free, quality planting material. Due to virus infections, vegetative propagation in gladiolus in the field is slow, and is a serious concern in the propagation of gladiolus. In vitro propagation provides an enormous increase in propagation rate and the ability to produce disease-free plant material. Numerous elements, including cultivars, explant type, size of explants, position of explants on medium, plant growth regulators and certain additives, incubation conditions, and sub-culturing time, all have a significant impact on in vitro clonal propagation of gladiolus plants as well as the development of in vitro cormel efficiency. There are certain obstacles and challenges that arise in the in vitro development of plants and the cormels of gladiolus. However, numerous studies and review reports on gladiolus for in vitro propagation have been reported, but very little is known about the factors influencing gladiolus’ in vitro effectiveness. In the present review, we focused on and analyzed research data accumulated over 50 years on diverse strategies for in vitro propagation such as direct, indirect organogenesis, and somatic embryogenesis, as well as various factors such as physical, nutritional, and hormonal influences on in vitro propagation, in vitro cormel formation efficiency, difficulties that arise, and new insights into in vitro development in gladiolus from the available literature worldwide. Future possibilities for further improvement in the in vitro propagation of ornamental gladiolus are also discussed. The current review provides insight into a comprehensive protocol for gladiolus in vitro propagation and emphasizes the importance of continuously advancing tissue culture techniques and factors influencing the in vitro efficiency towards improving in vitro plantlets and cormels in gladiolus (Gladiolus spp.).

Improved alpha shape-based continuum method for long-term density propagation

Improved alpha shape-based continuum method for long-term density propagation

Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

In the cooperative multi-sensor multi-vehicle (MSMV) localization domain, the data incest problem yields inconsistent data fusion results, thereby reducing the accuracy of vehicle localization. In order to address this problem, we propose the interval split covariance intersection filter (ISCIF). At first, the proposed ISCIF method is applied to the absolute positioning step. Then, we combine the interval constraint propagation (ICP) method and the proposed ISCIF method to realize relative positioning. Additionally, in order to enhance the robustness of the MSMV localization system, a Kullback–Leibler divergence (KLD)-based fault detection and exclusion (FDE) method is implemented in our system. Three simulations were carried out: Simulation scenarios 1 and 2 aimed to assess the accuracy of the proposed ISCIF with various capabilities of absolute vehicle positioning, while simulation scenario 3 was designed to evaluate the localization performance when faults were present. The simulation results of scenarios 1 and 2 demonstrated that our proposed vehicle localization method reduced the root mean square error (RMSE) by 8.9% and 15.5%, respectively, compared to the conventional split covariance intersection filter (SCIF) method. The simulation results of scenario 3 indicated that the implemented FDE method could effectively reduce the RMSE of vehicles (by about 55%) when faults were present in the system.

Custom-Trained Deep Learning-Based Auto-Segmentation for Male Pelvic Iterative CBCT on C-Arm Linear Accelerators

Purpose: To evaluate the clinical applicability of a commercial artificial intelligence (AI)-driven deep learning auto-segmentation (DLAS) tool on enhanced iterative cone-beam CT (iCBCT) acquisitions for intact prostate and prostate bed treatments. Methods and MaterialsDLAS models were trained using 116 iCBCT datasets with manually delineated organs-at-risk (OARs – bladder, femoral heads, and rectum) and target volumes (intact prostate and prostate bed) adhering to institution-specific contouring guidelines. An additional 25 intact prostate and prostate bed iCBCT datasets were utilized for model testing. Segmentation accuracy relative to a reference structure set was quantified using various geometric comparison metrics and qualitatively evaluated by trained physicists and physicians. These results were compared to those obtained for an additional DLAS-based model trained on planning CT (pCT) datasets and for a deformable image registration (DIR)-based automatic contour propagation method. Results: In most instances, statistically significant differences in the Dice similarity coefficient (DSC), 95% directed Hausdorff distance, and mean surface distance metrics were observed between the models, as the iCBCT-trained DLAS model outperformed the pCT-trained DLAS model and DIR-based method for all OARs and the intact prostate target volume. Mean DSC values for the proposed method were ≥0.90 for these volumes-of-interest. The iCBCT-trained DLAS model demonstrated a relatively suboptimal performance for the prostate bed segmentation, as the mean DSC value was <0.75 for this target contour. Overall, 90% of bladder, 93% of femoral head, 67% of rectum, and 92% of intact prostate contours generated by the proposed method were deemed clinically acceptable based on qualitative scoring, while approximately 63% of prostate bed contours required moderate or major manual editing to adhere to institutional contouring guidelines. Conclusion: The proposed method presents the potential for improved segmentation accuracy and efficiency when compared to the DIR-based automatic contour propagation method as commonly applied in CBCT-based dose evaluation and calculation studies.

Network Propagation-based Identification of Oligometastatic Biomarkers in Metastatic Colorectal Cancer

Background: The oligometastatic disease has been proposed as an intermediate state between primary tumor and systemically metastatic disease, which has great potential curable with locoregional therapies. However, since no biomarker for the identification of patients with true oligometastatic disease is clinically available, the diagnosis of oligometastatic disease remains controversial. Objective: We aim to identify potential biomarkers of colorectal cancer patients with true oligometastatic states, who will benefit most from local therapy. Methods: This study retrospectively analyzed the transcriptome profiles and clinical parameters of 307 metastatic colorectal cancer patients. A novel network propagation method and network-based strategy were combined to identify oligometastatic biomarkers to predict the prognoses of metastatic colorectal cancer patients. Results: We defined two metastatic risk groups according to twelve oligometastatic biomarkers, which exhibit distinct prognoses, clinicopathological features, immunological characteristics, and biological mechanisms. The metastatic risk assessment model exhibited a more powerful capacity for survival prediction compared to traditional clinicopathological features. The low-MRS group was most consistent with an oligometastatic state, while the high-MRS might be a potential polymetastatic state, which leads to the divergence of their prognostic outcomes and response to treatments. We also identified 22 significant immune check genes between the high-MRS and low- MRS groups. The difference in molecular mechanism between the two metastatic risk groups was associated with focal adhesion, nucleocytoplasmic transport, Hippo, PI3K-Akt, TGF-β, and EMCreceptor interaction signaling pathways. Conclusion: Our study provided a molecular definition of the oligometastatic state in colorectal cancer, which contributes to precise treatment decision-making for advanced patients.

FURION: modeling of FEL pulses propagation in dispersive soft X-ray beamline systems.

Modern X-ray free-electron lasers (XFELs) can generate pulses with durations ranging from femtoseconds to attoseconds. The numerical evaluation of ultra-short XFEL pulses through beamline systems is a critical process of beamline system design. However, the bandwidth of such ultra-short XFEL pulses is often non-negligible, and the propagation cannot be simply approximated using the central wavelength, especially in dispersive beamline systems. We developed a numerical model which is called Fourier optics based Ultrashort x-Ray pulse propagatION tool (FURION). This model can not only be used to simulate dispersive beamline systems but also to evaluate non-dispersive beamline systems. The FURION model utilizes Fresnel integral and angular spectrum integral to perform ultra-short XFEL pulse propagation in free space. We also present the method for XFEL pulse propagation through different types of dispersive gratings, which are commonly used in soft X-ray beamline systems. By using FURION, a start-to-end simulation of the FEL-1 beamline system at Shenzhen superconducting soft X-ray free electron laser (S3FEL) is carried out. This model can also be used to evaluate gratings-based spectrometers, beam splitters, pulse compressors, and pulse stretchers. This work provides valuable insights into the start-to-end simulation of X-ray beamline systems.

Application of the Polynomial Chaos Expansion to the Uncertainty Propagation in Fault Transients in Nuclear Fusion Reactors: DTT TF Fast Current Discharge

Nuclear fusion reactors are composed of several complex components whose behavior may be not certain a priori. This uncertainty may have a significant impact on the evolution of fault transients in the machine, causing unexpected damage to its components. For this reason, a suitable method for the uncertainty propagation during those transients is required. The Monte Carlo method would be the reference option, but it is, in most of the cases, not applicable due to the large number of required, repeated simulations. In this context, the Polynomial Chaos Expansion has been considered as a valuable alternative. It allows us to create a surrogate model of the original one in terms of orthogonal polynomials. Then, the uncertainty quantification is performed repeatedly, relying on this much simpler and faster model. Using the fast current discharge in the Divertor Tokamak Test Toroidal Field (DTT TF) coils as a reference scenario, the following method has been applied: the uncertainty on the parameters of the Fast Discharge Unit (FDU) varistor disks is propagated to the simulated electrical and electromagnetic relevant effects. Eventually, two worst-case scenarios are analyzed from a thermal–hydraulic point of view with the 4C code, simulating a fast current discharge as a consequence of a coil quench. It has been demonstrated that the uncertainty on the inputs (varistor parameters) strongly propagates, leading to a wide range of possible scenarios in the case of accidental transients. This result underlines the necessity of taking into account and propagating all possible uncertainties in the design of a fusion reactor according to the Best Estimate Plus Uncertainty approach. The uncertainty propagation from input data to electrical, electromagnetic, and thermal hydraulic results, using surrogate models, is the first of its kind in the field of the modeling of superconducting magnets for nuclear fusion applications.

Privacy-Preserving Identification of Cancer Subtype-Specific Driver Genes Based on Multigenomics Data with Privatedriver.

Identifying cancer subtype-specific driver genes from a large number of irrelevant passengers is crucial for targeted therapy in cancer treatment. Recently, the rapid accumulation of large-scale cancer genomics data from multiple institutions has presented remarkable opportunities for identification of cancer subtype-specific driver genes. However, the insufficient subtype samples, privacy issues, and heterogenous of aberration events pose great challenges in precisely identifying cancer subtype-specific driver genes. To address this, we introduce privatedriver, the first model for identifying subtype-specific driver genes that integrates genomics data from multiple institutions in a data privacy-preserving collaboration manner. The process of identifying subtype-specific cancer driver genes using privatedriver involves the following two steps: genomics data integration and collaborative training. In the integration process, the aberration events from multiple genomics data sources are combined for each institution using the forward and backward propagation method of NetICS. In the collaborative training process, each institution utilizes the federated learning framework to upload encrypted model parameters instead of raw data of all institutions to train a global model by using the non-negative matrix factorization algorithm. We applied privatedriver on head and neck squamous cell and colon cancer from The Cancer Genome Atlas website and evaluated it with two benchmarks using macro-Fscore. The comparison analysis demonstrates that privatedriver achieves comparable results to centralized learning models and outperforms most other nonprivacy preserving models, all while ensuring the confidentiality of patient information. We also demonstrate that, for varying predicted driver gene distributions in subtype, our model fully considers the heterogeneity of subtype and identifies subtype-specific driver genes corresponding to the given prognosis and therapeutic effect. The success of privatedriver reveals the feasibility and effectiveness of identifying cancer subtype-specific driver genes in a data protection manner, providing new insights for future privacy-preserving driver gene identification studies.

Reducing the propagation delay of compact block in Bitcoin network

SummaryBitcoin is a Blockchain‐based network in which thousands of nodes are directly connected and communicate via a gossip‐based flooding protocol. Mined blocks are propagated to all participating nodes in the network through a CBR (compact block relay) protocol developed to reduce the block propagation delay. However, propagation delay persists. The relay time between nodes must be measured and analyzed to determine the cause of the delay and provide solutions for reducing block propagation time. Previously, we measured the relay time and investigated the cause of the delay. According to the findings of the previous study, the delay of the relay time occurs when assembling compact blocks, depending on whether transactions are requested. In this paper, we find the reasons for requesting transactions. The reasons are due to the transaction propagation method and the characteristics of the transaction itself. We propose a solution based on this. It is a method of reducing probability of requesting transactions by using the compact block's “PREFILLEDTXN” to send the transactions expected to be requested with the block. The probability of requesting is reduced by up to 67% when transactions that have just entered the memory pool are propagated by PREFILLEDTXN. The block relay time is reduced by up to 44% as a result. Finally, this research reduces block relay time between nodes.