Time Dependence Research Articles

Time-series weather prediction in the Red sea using ensemble transformers

Accurately predicting Sea Surface Temperature (SST) and Wind Speed (WS) in the Red Sea is crucial for maritime safety, climate research, and ecosystem monitoring. However, modeling SST and WS is challenging due to the region’s complex and dynamic oceanic and atmospheric processes. This study introduces advanced predictive models based on Transformers to enhance the accuracy of SST and WS predictions by capturing time dependencies and modeling sequential data. A novel stacked architecture, named StackPred, improves the performance of individual Transformer models. Additionally, wavelet-based multiscale filtering is applied during preprocessing to separate relevant signals from noise, further enhancing prediction accuracy. The prediction performance is validated using publicly available SST and WS data from ten different locations in the Red Sea. Results consistently demonstrate the superiority of the proposed stacked deep learning models over existing techniques, including LSTM (Long Short-Term Memory), BiLSTM (Bidirectional LSTM), GRU (Gated Recurrent Unit), BIGRU (Bidirectional GRU), and single Transformers. Moreover, integrating wavelet denoising with StackPred significantly improves predictive performance compared to using data without denoising. The StackPred model achieves strong performance with an average R2 score of 99.83 for predicting SST and 99.96 for WS.

Effect of Peripheral Interventions in Patients with Peripheral Artery Disease Receiving Rivaroxaban and Aspirin: Analyses from the XATOA Registry

To assess the characteristics and clinical outcomes of patients with lower extremity peripheral artery disease (PAD) in XATOA receiving dual pathway inhibition (DPI) with rivaroxaban 2.5 mg twice daily plus aspirin according to lower extremity revascularisation (LER) history. XATOA is an international, multicentre, prospective, single arm registry study. This subanalysis investigated patients with lower extremity PAD according to LER history. Patients with coronary artery disease, PAD, or both, receiving DPI were followed for 12 or more months. Baseline characteristics and clinical outcomes were assessed according to LER history. A time dependency analysis assessed outcomes by time between the most recent LER procedure and the start of DPI. A multivariate analysis assessed the influence of patient characteristics on clinical outcomes. In XATOA (n = 5 532), 2 820 (51.0%) patients had lower extremity PAD, of whom 1 736 (61.6%) had prior LER and 1 084 (38.4%) had no prior LER. Baseline characteristics were generally similar between patients with or without prior LER. A higher proportion of patients with prior LER experienced any treatment emergent clinical events compared with those without prior LER (15.0% vs. 9.4%, respectively), with greater differences observed between incidence rates of limb events, including major adverse limb events (9.06 vs. 4.09 events per 100 patient years, respectively). Similar rates of myocardial infarction, stroke, and major bleeding were observed in both subgroups. Clinical event rates were generally higher in patients who had previous LER for six months or less compared with patients who had previous LET for more than six months before starting DPI, regardless of LER type. Multivariate analyses showed that prior LER was predictive of limb events. This subanalysis of XATOA found that prior LER was associated with increased rates of limb events, consistent with results of COMPASS and VOYAGER PAD. Rates of bleeding were also low regardless of LER history and consistent with the findings from these trials.

ASSESSMENT OF THE CORROSION-MECHANICAL PROPERTIES OF THE LEFT MATERIALS AND FORECASTING THE SAFETY OF GAS TURBINE PARTS

Purpose. Determination of the possibility of using the temperature-time parametric dependences of Miller-Larson, Manson-Haferd, and Sherby-Dorn for long-term prediction of the strength characteristics of parts subjected to oxidation and high-temperature sulfide-oxidation effects. Research methods. Long-term strength studies were performed on samples of ВЖЛ-12У and ЗМІ-3У alloys in synthetic ash (66.2 % Na2SO4, 20.4 % Fe2O3, 8.3 % NiO, 3.3 % CaO, 1.8 % V2O5) and an oxidizing environment at temperatures of 800 °C and 850 °C. The Miller-Larson, Manson-Haferd, and Sherby-Dorn temperature-time parametric dependences were used to predict the tensile strength of materials in an oxidizing and corrosive environment. The microstructure of the samples was studied using an optical microscope MIM-8M. Results. The results obtained indicate a high level of corrosion resistance of ЗМІ-3У samples at 800 °C and 850 °C and the possibility of determining the tensile strength of the material for a period of 1000, 5000, and 10000 hours by the calculation method. Tests of ВЖЛ-12У alloy samples in synthetic ash showed a discrepancy between the experimental time to fracture and the calculated value, regardless of the parametric method, which indicates a direct dependence of long-term strength on the degree of corrosion damage to the surface of the samples. Scientific novelty. The possibility of using the Miller-Larson, Manson-Haferd, and Sherby-Dorn temperature-time parametric dependences for long-term prediction of the strength characteristics of parts in an oxidizing environment has been confirmed. In a sulfide-oxide environment, the use of parametric methods to determine strength characteristics for up to 10,000 hours is possible only for corrosion-resistant alloys. Intensive corrosion damage to ВЖЛ-12У alloy leads to accelerated deformation, which limits the use of temperature and time dependencies for effective prediction of the time of failure of parts made of this material. Practical value. Determining a reliable method for predicting the strength characteristics of heat-resistant alloys in an oxidizing and corrosive environment allows reducing the time and financial costs of conducting lengthy field studies.

Mechanism and rate of anomalous extraction of chloride salts from water to 1,2-dichloroethane

Open circuit potential (OCP) measurements, potentiostatic pulse (PP) amperometry and cyclic voltammetry (CV), were used to investigate and to sort by rate the anomalous extraction of the chloride salts RCl of the cations R+ = tetrabutylammonium (TBA+), tetrapentylammonium (TPeA+), tetrahexylammonium (THexA+), tetraheptylammonium (THepA+) and bis(triphenyl-phosphoranylidene)ammonium (BA+) from water to 1,2-dichloroethane (DCE). A tentative mechanism is proposed comprising (a) the diffusion-controlled transport of the ion-pairs (RCl)ip from the aqueous to the organic solvent phase providing a major contribution to the chloride extraction, (b) decomposition of the multiple ion-pairs or clusters (RCl)ipx, which are formed by agglomeration of the single ion-pairs (RCl)ip in the aqueous phase (w), and (c) the exponential decay of the ion-pairs (RCl)ip accumulated on the organic solvent side of the interface at the beginning of the OCP measurements. This model enables to simulate successfully the experimental time dependence of the chloride concentration. A conclusion is made that the effect of the composition of the organic solvent electrolyte RX, where X- = tetraphenylborate (TPB-) or tetrakis(pentafluorophenyl) borate (TB-), on the rate and extent of the anomalous accumulation of the chloride anion in the phase (o) of the two-phase liquid system LiCl(w)/RX(o) can be related to the hydrophobicity of the electrolytes RCl and RX. The latter property can be characterized by the standard Gibbs energy of transfer from the aqueous (w) to the organic solvent (o) phase. The accumulation of the chloride anion in the phase (o) appears to be promoted by the decreasing value of the transfer Gibbs energy of RCl in the sequence TBACl > TPeACl > THexACl > THepACl > BACl, while the decreasing value of the transfer Gibbs energy of RX in the sequence TBATPB > TPeATPB > THexATPB > THepATPB > BATPB > BATB starts to block the cycle combining the energetically unfavorable extraction of RX from the phase (o) to the phase (w) with the energetically favorable extraction of RCl in the opposite direction. IR spectroscopy provides evidence of the formation of the water clusters in the organic solvent phase equilibrated with the aqueous phase in the presence of the tetraalkylammonium cation in both phases, which are supposed to drive the anomalous accumulation of the chloride anion in the phase (o).

ANALYSIS OF ROUTING PROTOCOLS CHARACTERISTICS IN AD-HOC NETWORK

Background. Wireless ad-hoc networks are becoming increasingly prevalence in remote areas, in extreme environments, even in military operations, and in scenarios where setting up infrastructure networks is not possible. Research of ad-hoc routing protocols problems allows improving the efficiency of their operation in conditions of high variability in packet loss or instability of network operation when the speed of users changes. Objective. The purpose of the paper is analysis of packet loss dependency from a network operation time, study of a user speed influence on a network efficiency, and research of network operation efficiency with different routing protocols. Methods. The method of routing protocols efficiency evaluation is the simulation of their operation in an ad-hoc network on a test data set and research of a network indicators dependency in time under different loads and changing mobility of users. Results. The conducted research demonstrated that user’s mobility at different speeds significantly affects the network operation as a whole. The instability of users' positions leads to a significant increase in route search time and packet transmission time. Among researched GPSR, DSDV, and AODV protocols, the latter proved to be the best because it has the lowest percentage of data loss and the lowest average time of message send and receive operations. Conclusions. The work is dedicated to the actual problem of developing and setting parameters of ad-hoc network. Received research results indicate the need to choose the optimal routing protocol depending on specific application conditions, such as user movement speed and network stability. The proposed solutions can be the first stage of complex processing of packets in the mobile network and justify the choice of AODV protocol as a basis for further improvement.

Investigation of the temperature dependence of the dielectric relaxation of chlorobenzene, bromobenzene and iodobenzene

This paper deals with the investigation results of temperature dependence of complex permittivity’sε∗ = έ − iε˝ of chloro, bromo and iodo- benzene at wavelength 11,50 cm and 12,80 cm within the temperature range 20 ÷ -80˚. It is established that dielectric properties of given liquids are characteristic of one relaxation time. The dielectric spectra were studied taking into account other frequencies given in the literature. In all investigated liquids, a jump appears in the real and imaginary parts of the complex permittivity at the point of the phase transition. The temperature dependence of the dielectric relaxation time of molecules in the liquid state has been determined. It was found that with an increase in the amount of the halogen substituent, an increase in the relaxation time occurs. Macroscopic and molecular relaxation times were calculated from the experimental values. The thermodynamic quantities characterizing the process of dielectric relaxation are calculated. The activation enthalpy of dielectric relaxation processes is compared. The studies were carried out using the dielectric spectroscopy method. This method allows a more detailed study of the dielectric properties of the objects of study due to the large equilibrium (“static”) dielectric constant of the object. It has been determined that the height of the potential barrier separating the two equilibrium positions of a polar molecule is greatest in the state of a pure polar liquid and decreases with dilution in a non-polar solvent.

Analytical description of the ionic relaxation in the presence of surface adsorption

The temporal evolution of the surface electric field in a cell in the presence of ionic adsorption is investigated. The analysis is performed in the framework of the Poisson–Nernst–Planck model, based on the conservation of particles and the equation of Poisson for the actual electric potential across the cell, assuming that only the ions of a given sign are mobile. The adsorption is described using a kinetic equation of Langmuir’s type. The simple case of small adsorption is considered, in which the saturation effect can be neglected, and the fundamental equations of the model can be linearized. In this framework, the effective relaxation time describing the dynamics of the system is evaluated, as well as the profiles of the ions and the electric field. The case in which the sample is a half-space is first considered. A more realistic situation where the sample is a slab of thickness d, limited by two identical or different electrodes, is analyzed too. The difference in electric potential due to the adsorption phenomenon between the electrodes is determined. Our analysis shows that its time dependence, when the electrodes have different adsorption properties, is not monotonic.

Deconvoluting Monomer- and Dimer-Specific Distance Distributions between Spin Labels in a Monomer/Dimer Mixture Using T1-Edited DEER EPR Spectroscopy.

Double electron-electron resonance (DEER) EPR is a powerful tool in structural biology, providing distances between pairs of spin labels. When the sample consists of a mixture of oligomeric species (e.g., monomer and dimer), the question arises as to how to assign the peaks in the DEER-derived probability distance distribution to the individual species. Here, we propose incorporating an EPR longitudinal electron relaxation (T1) inversion recovery experiment within a DEER pulse sequence to resolve this problem. The apparent T1 between dipolar coupled electron spins measured from the inversion recovery time (τinv) dependence of the peak intensities in the T1-edited DEER-derived probability P(r) distance distribution will be affected by the number of nitroxide labels attached to the biomolecule of interest, for example, two for a monomer and four for a dimer. We show that global fitting of all the T1-edited DEER echo curves, recorded over a range of τinv values, permits the deconvolution of distances between spin labels originating from monomeric (longer T1) and dimeric (shorter T1) species. This is especially useful when the trapping of spin labels in different conformational states during freezing gives rise to complex P(r) distance distributions. The utility of this approach is demonstrated for two systems, the β1 adrenergic receptor and a construct of the huntingtin exon-1 protein fused to the immunoglobulin domain of protein G, both of which exist in a monomer-dimer equilibrium.

Mesoscopic elasticity controls dynamin-driven fission of lipid tubules

Mesoscale physics bridges the gap between the microscopic degrees of freedom of a system and its large-scale continuous behavior and highlights the role of a few key quantities in complex and multiscale phenomena, like dynamin-driven fission of lipid membranes. The dynamin protein wraps the neck formed during clathrin-mediated endocytosis, for instance, and constricts it until severing occurs. Although ubiquitous and fundamental for life, the cooperation between the GTP-consuming conformational changes within the protein and the full-scale response of the underlying lipid substrate is yet to be unraveled. In this work, we build an effective mesoscopic model from constriction to fission of lipid tubules based on continuum membrane elasticity and implicitly accounting for ratchet-like power strokes of dynamins. Localization of the fission event, the overall geometry, and the energy expenditure we predict comply with the major experimental findings. This bolsters the idea that a continuous picture emerges soon enough to relate dynamin polymerization length and membrane rigidity and tension with the optimal pathway to fission. We therefore suggest that dynamins found in in vivo processes may optimize their structure accordingly. Ultimately, we shed light on real-time conductance measurements available in literature and predict the fission time dependency on elastic parameters.

Lessons learned: avoiding bias via multi-state analysis of patients’ trajectories in real-time

ObjectivesMany studies have attempted to determine the disease severity and patterns of COVID-19. However, at the beginning of the pandemic, the complex patients’ trajectories were only descriptively reported, and many analyses were worryingly prone to time-dependent-, selection-, and competing risk biases. Multi-state models avoid these biases by jointly analysing multiple clinical outcomes while taking into account their time dependency, including current cases, and modelling competing events. This paper uses a publicly available data set from the first wave in Israel as an example to demonstrate the benefits of analysing hospital data via multi-state methodology.MethodsWe compared the outcome of the data analysis using multi-state models with the outcome obtained when various forms of bias are ignored. Furthermore, we used Cox regression to model the transitions among the states in a multi-state model. This allowed for the comparison of the covariates’ influence on transition rates between the two states. Lastly, we calculated expected lengths of stay and state probabilities based on the multi-state model and visualised it using stacked probability plots.ResultsCompared to standard methods, multi-state models avoid many biases in the analysis of real-time disease developments. The utility of multi-state models is further highlighted through the use of stacked probability plots, which visualise the results. In addition, by stratification of disease patterns by subgroups and visualisation of the distribution of possible outcomes, these models bring the data into an interpretable form.ConclusionTo accurately guide the provision of medical resources, this paper recommends the real-time collection of hospital data and its analysis using multi-state models, as this method eliminates many potential biases. By applying multi-state models to real-time data, the gained knowledge allows rapid detection of altered disease courses when new variants arise, which is essential when informing medical and political decision-makers as well as the general population.

Edge-dependent anomalous topology in synthetic photonic lattices subject to discrete step walks

Anomalous topological phases, where edge states coexist with topologically trivial Chern bands, can only appear in periodically driven lattices. When the driving is smooth and continuous, the bulk-edge correspondence is guaranteed by the existence of a bulk invariant known as the winding number. However, in lattices subject to periodic discrete step walks the existence of edge states does not only depend on bulk invariants but also on the boundary. This is a consequence of the absence of an intrinsic time dependence or micromotion in discrete step walks. We report the observation of edge states and a simultaneous measurement of the bulk invariants in anomalous topological phases in a two-dimensional synthetic photonic lattice subject to discrete step walks. The lattice is implemented using time multiplexing of light pulses in two coupled fibre rings, in which one of the dimensions displays real-space dynamics and the other one is parametric. The presence of edge states is inherent to the periodic driving and depends on the properties of the boundary in the implemented two-band model with zero Chern number. We provide a suitable expression for the topological invariants whose calculation does not rely on micromotion dynamics. Published by the American Physical Society 2024

An Agile Adaptive Biased-Randomized Discrete-Event Heuristic for the Resource-Constrained Project Scheduling Problem

In industries such as aircraft or train manufacturing, large-scale manufacturing companies often manage several complex projects. Each of these projects includes multiple tasks that share a set of limited resources. Typically, these tasks are also subject to time dependencies among them. One frequent goal in these scenarios is to minimize the makespan, or total time required to complete all the tasks within the entire project. Decisions revolve around scheduling these tasks, determining the sequence in which they are processed, and allocating shared resources to optimize efficiency while respecting the time dependencies among tasks. This problem is known in the scientific literature as the Resource-Constrained Project Scheduling Problem (RCPSP). Being an NP-hard problem with time dependencies and resource constraints, several optimization algorithms have already been proposed to tackle the RCPSP. In this paper, a novel discrete-event heuristic is introduced and later extended into an agile biased-randomized algorithm complemented with an adaptive capability to tune the parameters of the algorithm. The results underscore the effectiveness of the algorithm in finding competitive solutions for this problem within short computing times.

Spatial and temporal dynamics of thermal motion of a chain of liquid lead nanoinclusions attached to a fixed dislocation segment in an aluminum matrix

Thermal motion of a chain of liquid lead nanoinclusions attached to a dislocation segment fixed at its ends in the aluminum-based alloy was studied in situ using TEM in the temperature range from 442 °C to 497 °C. The projections of points of the trajectories of the inclusions onto the dislocation line were analyzed. The evidence for the collective interaction of all the inclusions and their spatially correlated collective thermal motion was obtained. Also, moderate temporal autocorrelation of thermal motion of the inclusions and a very weak temporal cross-correlation of thermal motion of the inclusions in all pairs was shown. The FFT phase spectra of the time dependencies of the inclusions positions on the dislocation segment give the evidence for the phase synchronization of thermal oscillations of the inclusions. It is shown that the thermal oscillations of the terminal inclusion attached to the dislocation node and elevation of temperature lead to decrease in the level of synchronization of thermal oscillations of the inclusions in the chain. It is indicated that the development of the process of desynchronization of thermal oscillations of the inclusions manifests itself by an increase in the density of phase steps in the initially linear unwrapped phase spectra accompanying by an increase in the interaction of the steps.

An Analytical Model for Describing Transient Photocurrents in Bias‐Assisted Charge Extraction for Low‐Mobility Organic Solar Cells

Bias‐assisted charge extraction (BACE) is a powerful technique for measuring the carrier density in organic solar cells under operational conditions and to deduce information on the charge recombination properties. Hereby, the carrier density in the active layer device is determined by integrating the transient extraction current, while nongeminate recombination during the charge extraction processes is generally neglected. This assumption becomes questionable for low mobilities, for example, at low temperatures and in case of high energetic disorder. Herein, the extraction process in BACE measurement is investigated by incorporating both drift and bimolecular recombination of charges during charge extraction into a unified framework. An explicit analytical model is proposed to describe the time dependence of the transient photocurrent in BACE measurement and excellent agreement is found with drift‐diffusion simulations. It is shown that global fitting of transient photocurrents measured at various collection biases with this analytical model allows to determine the carrier density in the device with high accuracy. Furthermore, the analytical model is demonstrated to provide accurate mobilities for both the fast and slow carriers within the device, rendering it a valuable supplement to other mobility measurement techniques such as resistance‐dependent photovoltage and space–charge‐limited current.

Finite-size scaling in kinetics of phase separation in certain models of aligning active particles.

To study the kinetics of phase separation in active matter systems, we consider models that impose a Vicsek-type self-propulsion rule on otherwise passive particles interacting via the Lennard-Jones potential. Two types of kinetics are of interest: one conserves the total momentum of all the constituents and the other does not. We carry out molecular dynamics simulations to obtain results on structural, growth, and aging properties. Results from our studies, with various finite boxes, show that there exist scalings with respect to the system sizes, in both the latter quantities, as in the standard passive cases. We have exploited this scaling picture to accurately estimate the corresponding exponents, in the thermodynamically large system size limit, for power-law time dependences. It is shown that certain analytical functions describe the behavior of these quantities quite accurately, including the finite-size limits. Our results demonstrate that even though the conservation of velocity has at best weak effects on the dynamics of evolution in the thermodynamic limit, the finite-size behavior is strongly influenced by the presence (or the absence) of it.

Predicting the Color Polymorphism of ROY from a Time-Dependent Optimally Tuned Screened Range-Separated Hybrid Functional.

Polymorphism is a well-known property of molecular crystals, which allows the same molecule to form solids with several crystalline structures that can differ significantly in physical properties. Polymorphs that possess different optical absorption properties in the visible range may exhibit different perceived colors, a phenomenon known as color polymorphism. One striking example of color polymorphism is given by 5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile, known as ROY for its red-orange-yellow colors. First-principles prediction of color polymorphism may help in polymorph assignment and design but has proven to be challenging. Here, we predict the absorption spectra and simulate the colors of 12 ROY polymorphs using the general, nonempirical method of time-dependent (TD) optimally tuned screened range-separated hybrid (OT-SRSH) functional. For 5 ROY polymorphs with known experimental absorption spectra, we show that the TD-OT-SRSH approach predicts absorption spectra in quantitative agreement with experiment. For all polymorphs, we show that an accurate simulation of the colors is obtained, paving the way to a fully predictive, low-cost calculation of color polymorphism.

Predicting radiation-induced immune suppression in lung cancer patients treated with stereotactic body radiation therapy.

Stereotactic body radiation therapy (SBRT) is known to modulate the immune system and contribute to the generation of anti-tumor T cells and stimulate T cell infiltration into tumors. Radiation-induced immune suppression (RIIS) is a side effect of radiation therapy that can decrease immunological function by killing naive T cells as well as SBRT-induced newly created effector T cells, suppressing the immune response to tumors and increasing susceptibility to infections. RIIS varies substantially among patients and it is currently unclear what drives this variability. Models that can accurately predict RIIS in near real time based on treatment plan characteristics would allow treatment planners to maintain current protocol specific dosimetric criteria while minimizing immune suppression. In this paper, we present an algorithm to predict RIIS based on a model of circulating blood using early stage lung cancer patients treated with SBRT. This Python-based algorithm uses DICOM data for radiation therapy treatment plans, dose maps, patient CT data sets, and organ delineations to stochastically simulate blood flow and predict the doses absorbed by circulating lymphocytes. These absorbed doses are used to predict the fraction of lymphocytes killed by a given treatment plan. Finally, the time dependence of absolute lymphocyte count (ALC) following SBRT is modeled using longitudinal blood data up to a year after treatment. This model was developed and evaluated on a cohort of 64 patients with 10-fold cross validation. Our algorithm predicted post-treatment ALC with an average error of cells/L with 89% of the patients having a prediction error below 0.5×109 cells/L. The accuracy was consistent across a wide range of clinical and treatment variables. Our model is able to predict post-treatment ALC<0.8 (grade 2 lymphopenia), with a sensitivity of 81% and a specificity of 98%. This model has a ∼38-s end-to-end prediction time of post treatment ALC. Our model performed well in predicting RIIS in patients treated using lung SBRT. With near-real time model prediction time, it has the capability to be interfaced with treatment planning systems to prospectively reduce immune cell toxicity while maintaining national SBRT conformity and plan quality criteria.

The small tumor antigen of Merkel cell polyomavirus accomplishes cellular transformation by uniquely localizing to the nucleus despite the absence of a known nuclear localization signal

BackgroundMerkel Cell Carcinoma (MCC) is an aggressive skin cancer that is three times deadlier than melanoma. In 2008, it was found that 80% of MCC cases are caused by the genomic integration of a novel polyomavirus, Merkel Cell Polyomavirus (MCPyV), and the expression of its small and truncated large tumor antigens (ST and LT-t, respectively). MCPyV belongs to a family of human polyomaviruses; however, it is the only one with a clear association to cancer.MethodsTo investigate the role and mechanisms of various polyomavirus tumor antigens in cellular transformation, Rat-2 and 293A cells were transduced with pLENTI MCPyV LT-t, MCPyV ST, TSPyV ST, HPyV7 ST, or empty pLENTI and assessed through multiple transformation assays, and subcellular fractionations. One-way ANOVA tests were used to assess statistical significance.ResultsSoft agar, proliferation, doubling time, glucose uptake, and serum dependence assays confirmed ST to be the dominant transforming protein of MCPyV. Furthermore, it was found that MCPyV ST is uniquely transforming, as the ST antigens of other non-oncogenic human polyomaviruses such as Trichodysplasia Spinulosa-Associated Polyomavirus (TSPyV) and Human Polyomavirus 7 (HPyV7) were not transforming when similarly assessed. Identification of structural dissimilarities between transforming and non-transforming tumor antigens revealed that the uniquely transforming domain(s) of MCPyV ST are likely located within the structurally dissimilar loops of the MCPyV ST unique region. Of all known MCPyV ST cellular interactors, 62% are exclusively or transiently nuclear, suggesting that MCPyV ST localizes to the nucleus despite the absence of a canonical nuclear localization signal. Indeed, subcellular fractionations confirmed that MCPyV ST could achieve nuclear localization through a currently unknown, regulated mechanism independent of its small size, as HPyV7 and TSPyV ST proteins were incapable of nuclear translocation. Although nuclear localization was found to be important for several transforming properties of MCPyV ST, some properties were also performed by a cytoplasmic sequestered MCPyV ST, suggesting that MCPyV ST may perform different transforming functions in individual subcellular compartments.ConclusionsTogether, these data further elucidate the unique differences between MCPyV ST and other polyomavirus ST proteins necessary to understand MCPyV as the only known human oncogenic polyomavirus.

Nomogram for Predicting Survival in Locally Advanced Cervical Cancer with Concurrent Chemoradiotherapy plus or Not Adjuvant Chemotherapy: A Retrospective Analysis Based on 2018 FIGO Staging.

Background: The comprehensive treatment mode of combining concurrent chemoradiotherapy (CCRT) with adjuvant chemotherapy (AC) is a commonly used mainstream model in the clinical practice of locally advanced cervical cancer (LACC). However, the necessity for AC after CCRT lacks sufficient evidence-based medical support. This study constructs a predictive model for the survival time dependence of CCRT ± AC for LACC based on the 2018 International Federation of Gynecology and Obstetrics (FIGO) staging with internal validation, the prognosis was assessed with intensity-modulated radiotherapy (IMRT) and concurrent cisplatin, and provides guidance for future stratified treatment. Materials and Methods: The retrospective analysis included 482 patients with LACC who CCRT from January 2016 to January 2023. Patients who used the 2009 FIGO staging were all standardized for the 2018 FIGO staging. The 482 patients with LACC were divided into a training set (n = 290) and a validation set (n = 192) at a ratio of 6:4. COX multivariate regression model and LASSO regression were used to screen for independent prognostic factors affecting progression-free survival (PFS) and overall survival (OS), and a nomogram clinical prediction model was constructed based on these factors. Evaluate the effectiveness of the model through the receiver operating characteristic curve, calibration curve, decision curve, risk heat map, and survival curves for risk stratification. Results: The PFS and OS independent prognostic risk factors affecting the 2018 FIGO staging of LACC during CCRT were validated to be similar to the 2009 FIGO staging prediction model reported in previous literature. In the training cohort, area under the curve (AUC) values at 1, 3, and 5 years were 0.941, 0.882, and 0.885 for PFS, and 0.946, 0.946, and 0.969 for OS, respectively. When applied to a test cohort, the model also showed accurate prediction result (AUC at 1, 3, and 5 years were 0.869, 0.891, and 0.899 for PFS, and 0.891, 0.941 and 0.878 for OS, respectively). Subgroup analysis suggests that patients with LACC, adenocarcinoma, stage IVA, pelvic lymph node metastasis, pretreatment hemoglobin ≤100 g/l and residual tumor diameter >2 cm, who received CCRT in the 2018 FIGO stage, may benefit more from adjuvant chemtherapy. Conclusions: Based on the 2018 FIGO staging, a nomogram prediction model for PFS and OS in patients with LACC undergoing CCRT was developed. The model, established by combining weighted clinical and pathological factors, can provide more personalized treatment predictions in clinical practice. For patients with high-risk factors such as residual tumor diameter > 2 cm after CCRT for LACC, AC may bring benefits.

Time dependency for HPV ctDNA detection as a prognostic biomarker for anal cancer.

3513 Background: Prior HPV infection is associated with &gt; 90% of anal cancers, a malignancy with rising incidence in the United States. Standard treatment for localized anal cancer is concurrent chemoradiation (cRT). While detection of circulating tumor DNA (ctDNA) within weeks after surgery is linked to recurrence in other solid tumors, the optimal time point for ctDNA detection as a prognostic biomarker following cRT for HPV-associated anogenital cancers is not well characterized. We utilized a novel, highly sensitive HPV ctDNA assay to evaluate clinical outcomes according to HPV ctDNA status among patients with localized anal cancer treated with cRT. Methods: ctDNA was isolated from patients with stages I-III anal cancer treated at our institution with cRT prior to treatment, at the end of treatment (week 5), and at months 3, 6, 9, and 12 after treatment under an IRB-approved protocol. A droplet digital HPV ctDNA PCR assay evaluating HPV E6 and E7 oncogenes for 13 oncogenic HPV types was utilized for quantification of HPV ctDNA. A limit of quantification at 16 HPV copies/mL plasma was set for “HPV ctDNA detection.” Median recurrence-free survival (RFS) according to HPV ctDNA status was estimated via Kaplan-Meier and compared using a log-rank test. Associations between selected clinical factors and recurrence were evaluated with a Chi-square test, with a one-sided p &lt; .05 considered significant. Results: Following cRT, HPV ctDNA was detected in 9/41 (22%), 4/30 (13%), 2/20 (10%), and 0/12 (0%) patients at week 5 and months 3, 6, and 12, respectively. Detection of HPV ctDNA 3 months after cRT was associated with clinical recurrence (100% versus 8%; odds ratio 88, 95% CI 4-2000; p = .006) and inferior RFS (5.9 months vs not reached (NR); hazard ratio (HR) 24, 95% CI 1.2-475; p &lt; .001) relative to HPV ctDNA-negative status. Differences in RFS according to HPV ctDNA status were not observed at week 5/end of treatment (median RFS NR for both; HR 2.4, 95% CI .5-10; p = .15). At month 3, sensitivity and specificity for recurrence according to HPV ctDNA detection were 57% and 100%, respectively, with a PPV and NPV of 100% and 89%, respectively. Baseline clinical stage, T stage, N stage, age, and gender were not associated with clinical recurrence after cRT (p &gt; .25 for all). Conclusions: With a novel, highly sensitive assay, detection of HPV ctDNA at 3 months after cRT was associated with RFS. Further, an HPV ctDNA-positive status outperformed baseline clinical features for prognosticating anal cancer recurrence after cRT in this retrospective series. Future clinical trials should incorporate the 3-month post-treatment time point for identification of patients with HPV-positive anal cancer at elevated risk for recurrence according to HPV ctDNA status. A clinical trial evaluating anti-PD-L1 + anti-TIGIT immunotherapy for patients with HPV-associated cancers with detected HPV ctDNA after cRT is forthcoming at our institution.