Optimal Sequence Research Articles

Improving Health Outcomes Through Treatment Sequencing Optimization in Multiple Myeloma: A Simulation Model in Transplant-Ineligible Patients.

Patients with multiple myeloma often require multiple treatment lines. The order in which treatments are sequenced has impact on clinical outcomes. This study aimed to estimate progression-free survival (PFS) and overall survival (OS) with common treatment sequences used in Portugal and the incremental benefit of an optimal sequence in transplant-ineligible patients with multiple myeloma. A state-transition sequential model with a five-health state conceptual structure was developed to simulate and compare survival outcomes between treatment sequences up to four lines of treatments. Data sources included randomized clinical trials and indirect treatment comparisons. A panel of Portuguese hematologists listed four most common treatment sequences and optimal sequence of choice in transplant-ineligible patients. Our simulation estimated an OS between 6.1 and 7.8 years using the most common sequences, with VMP + DRd + Pd + Kd as the most effective (7.8 years). Optimal sequence of choice (DRd + PVd + Kd + Vd) achieved OS of 9.8 years and may extend OS in 2.0-3.7 years vs. most common sequences (26%-61% increase). This benefit was mostly explained by extended PFS in the first line of treatment. Model results demonstrate that choosing the most effective treatment upfront is crucial in delaying disease progression thus yielding better survival outcomes in transplant-ineligible patients. There was a clear survival benefit in using daratumumab-based regimens in first line. This modelling exercise highlights the need to raise awareness around the impact of sequencing strategies to improve patient's outcomes.

Optimal Sequence for Total Neoadjuvant Therapy in Locally Advanced Rectal Cancer: An Evidence-Based Review.

Historically, multimodal therapeutic strategies involving preoperative chemoradiotherapy (CRT), surgery, and adjuvant chemotherapy (CT) have been employed to treat locally advanced rectal cancer (LARC). Total Neoadjuvant Therapy (TNT) is showing promise in improving outcomes. Despite its benefits, the optimal sequencing within TNT-whether induction chemotherapy should precede or follow chemoradiotherapy-remains a critical question. This study endeavors to explore the effects of different TNT sequencing strategies on patient outcomes, including tumor downstaging, pathological response, organ preservation, and the balance between efficacy and tolerability. Our methodology entailed a comprehensive literature review conducted on Medline, focusing on recent research, including retrospective studies, systematic reviews, and clinical trials. The review emphasized the comparison of induction chemotherapy versus consolidation chemotherapy within TNT regimens, assessing outcomes such as pathological response, organ preservation rates, and adverse effects. To ensure the selection of appropriate and high-quality studies, specific inclusion and exclusion criteria were applied. The analysis revealed that induction chemotherapy might lead to decreased adherence to subsequent chemoradiotherapy while offering an early intervention against micrometastasis and potentially improving overall chemotherapy compliance. Conversely, consolidation chemotherapy has been associated with higher pathological complete response (pCR) rates and improved tolerability, indicating its potential for patients requiring local symptom relief or those eligible for a nonoperative management approach. Comparative studies like CAO/ARO/AIO-12 and the OPRA trials have significantly contributed to our understanding, suggesting that while both strategies have distinct advantages, the choice between induction and consolidation chemotherapy should be tailored based on individual patient profiles and tumor characteristics. This narrative review underscores the importance of a nuanced approach to TNT sequencing in locally advanced rectal cancer, highlighting the need for further research to refine treatment strategies.

Engineered mRNAs With Stable Structures Minimize Double-stranded RNA Formation and Increase Protein Expression

The therapeutic use of synthetic message RNA (mRNA) has been validated in COVID-19 vaccines and shows enormous potential in developing infectious and oncological vaccines. However, double-stranded RNA (dsRNA) byproducts generated during the in vitro transcription (IVT) process can diminish the efficacy of mRNA-based therapeutics and provoke innate immune responses. Existing methods to eliminate dsRNA byproducts are often cumbersome and labor-intensive. In this study, we revealed that a loose mRNA secondary structure and more unpaired U bases in the sequence generally lead to the formation of more dsRNA byproducts during the IVT process. We further developed a predictive model for dsRNA byproducts formation based on sequence characteristics to guide the optimization of mRNA sequences, helping to minimize unwanted immune response and improve the protein expression of mRNA products. Collectively, our study provides novel clues and methodologies for developing effective mRNA therapeutics with minimized dsRNA byproducts and increased protein expression.

Security-Aware Deadline Constraint Task Scheduling using Hybrid Optimization of Modified Flying Squirrel Genetic Chameleon Swarm Algorithm

Cloud computing enables cost-effective resource sharing in hybrid clouds to tackle the problem of insufficient resources by elastically scaling the service capability based on the users’ needs. However, task scheduling (TS) in cloud environments is challenging due to deadline-based performance and security constraints. To remove the existing drawbacks based on deadline and security constraints, a Security-Aware Deadline Constraint TS (SADCTS) approach is presented using a hybrid optimization algorithm of the Modified Flying Squirrel Genetic Chameleon Swarm Algorithm (MFSGCSA). The proposed MFSGCSA is developed by integrating the genetic operators into CSA and combining it with the modified Flying Squirrel Optimization (FSO) algorithm in which the position update and global search equations are enhanced by adaptive probability factor to reduce the local optimum problem. In this SADCTS approach, the task assignment process is modeled into an NP-hard problem concerning a multi-level security model using user authentication, integrity, and confidentiality. This maximizes tasks’ completion rate and decreases the resource costs to process tasks with different deadline limitations. The optimal schedule sequence is obtained by MFSGCSA, where tasks are scheduled concurrently based on security constraints, demand, and deadlines to improve the prominence of cost, energy, and makespan. Experiments are simulated using the CloudSim toolkit, and the comparative outcomes show that the suggested SADCTS approach reduced the makespan, cost, and energy by 5-20% more than the traditional methods. Thus, SADCTS provides less task violation of 0.0001%, high energy efficiency of 700GHz/W, high resource utilization of 92%, less cost efficiency of 72GHz/$, and less makespan of 480minutes to satisfy the necessary security and deadline requirements for TS in shared resource hybrid clouds.

Prediction-Based Submarine Cable-Tracking Strategy for Autonomous Underwater Vehicles with Side-Scan Sonar

This study investigates the tracking of underwater cables using autonomous underwater vehicles (AUVs) equipped with side-scan sonar (SSS). AUV motion stability is crucial for effective SSS imaging, which is essential for continuous cable tracking. Traditional methods that derive AUV guidance rates directly from measured cable states often cause unnecessary jitter when imaging, complicating accurate detection. To address this, we propose a non-myopic receding-horizon optimization (RHO) strategy designed to maximize cable imaging quality while considering AUV maneuvering constraints. This strategy identifies the optimal heading decision sequence over a future horizon, ensuring stable and efficient cable tracking. We also employ a long short-term memory (LSTM) network to predict future cable states, further minimizing AUV motion instability during abrupt path changes. Given the computational limitations of AUVs, we have developed an efficient decision-making framework that can execute resource-intensive algorithms in real time. Finally, the robustness and effectiveness of the proposed algorithm were validated through comparative experiments. The results demonstrate that the proposed method outperforms existing methods in key metrics such as cable-tracking accuracy and AUV motion stability. This ensures that the AUV can acquire high-quality acoustic images of the submarine cable in an optimal state, enhancing the continuity and reliability of cable-tracking tasks.

Real-world (rw) treatment (tx) patterns and overall survival (OS) in patients (pts) with locally advanced/metastatic urothelial carcinoma (la/mUC) treated with avelumab first-line maintenance (1LM): Updated results from the SPEAR Bladder II study.

393 Background: Avelumab 1LM tx is approved in the US for pts with la/mUC who do not have disease progression after platinum-based chemotherapy (PBC). The tx landscape is evolving due to the recent approvals of newer therapies for pts with la/mUC. This study examines rw tx patterns, tx sequencing post avelumab 1LM, and OS in pts with la/mUC who initiated 1L tx in the US community oncology setting. Methods: This retrospective cohort study used the US Oncology Network (iKnowMed) EHR database. The study included adult pts (≥18 years) with la/mUC initiating 1L tx (index date) between Dec 1, 2019, and Nov 30, 2023. Pts were censored for OS at their last contact date or the end of study period, Feb 28, 2024. Descriptive statistics were used to describe tx patterns. Kaplan-Meier analysis was used to evaluate OS from the start of 1LM or second-line (2L) enfortumab vedotin (EV) treatment post avelumab 1LM. Results: A total of 1,658 pts with la/mUC initiated 1L tx with a median (range) follow-up of 9.0 months (0.1-50.4). The median (range) age at diagnosis was 73 years (31-90+), 74.7% were male and 47.1% had ECOG performance status ≤1. The 1L therapies included: IO monotherapy (41.2%), PBC only (32.4%), PBC followed by avelumab 1LM (11.2%), and other tx (15.1%). Since the US FDA approval (June 30, 2020), the utilization of avelumab 1LM among 1L PBC users ranged from 25.0% to 32.9% in 2023. Median (range) follow-up from the start of avelumab 1LM was 9.1 months (0.5-42.2). During the study observation period, 44 pts (23.7%) remained on avelumab 1LM. Median (95% CI) OS from start of avelumab 1LM was 18.5 months (13.8-23.8). Among the overall study cohort, 598 (36.1%) and 196 pts (11.8%) received 2L and third-line tx, respectively. The most common 2L therapies were IO monotherapy (44.1%) and EV monotherapy (23.9%). After discontinuation of avelumab 1LM, 81 pts (43.5%) received 2L tx, 48 (59.3%) of those received EV; median OS from start of 2L EV was 12.7 months (7.2-16.5). Conclusions: These rw study findings demonstrate avelumab 1LM effectiveness for pts whose disease has not progressed on 1L PBC and provides evidence of the use of 2L EV after avelumab 1LM. There was an increased uptake of avelumab 1LM since its FDA approval. Survival outcomes are consistent with the JAVELIN Bladder 100 clinical trial and other rw studies that had shorter follow-up. We observed high IO monotherapy use despite the platinum-ineligible population typically making up 11% of pts with la/mUC. 1 Some of these pts treated with IO monotherapy may benefit from alternative 1L regimens. Future rw studies are needed to further characterize contemporary tx patterns, optimal tx sequencing, and survival in pts with la/mUC amidst the evolving tx landscape. 1 Gupta S, et al. JNCI 2024;116(4):547–554.

Surface structures and wettability: Their roles in enhancing nucleate boiling heat transfer of refrigerants

Nucleate boiling is an effective heat transfer mechanism. Previous studies have demonstrated that modifying surface structure or wettability can independently improve nucleate boiling efficiency; however, the combined effects and optimal modification sequence remain underexplored. This research aims to comprehensively analyze the influence of surface structure and wettability on the nucleate boiling heat transfer performance of refrigerants. Different surface structures and wettability were prepared using surface polishing, laser ablation, mechanical cutting, and a combination that involved initially employing laser ablation, followed by the application of chemical etching, and finishing with the treatment using materials possessing low surface energy. The static contact angles on these surfaces were measured via the capillary rise method, followed by pool boiling experiments and bubble behavior visualization. The results show that increasing surface wettability can enhance bubble departure frequency and capillary wicking, thereby improving critical heat flux and heat transfer coefficient. The key to optimizing surface modification to enhance critical heat flux involves constructing alternating high and low nucleation core regions to reduce interactions between bubbles; for enhancing heat transfer coefficient, the key lies in constructing compact nanoscale structures that expand the heat transfer and nucleation area, followed by improving wettability.

The Role of CAR T-Cell Therapy in Relapsed/Refractory Adult B-ALL.

CAR T-cell therapy is a recent therapeutic advancement that has transformed the management of relapsed/refractory (R/R) B-cell acute lymphoblastic leukemia (B-ALL). To date, there are 2 FDA-approved CAR-T products for R/R B-ALL: tisagenlecleucel in patients aged <26 years and brexucabtagene autoleucel in those aged ≥18 years. This review summarizes the pivotal clinical trials that led to FDA approval of these 2 products and highlight emerging data addressing key questions pertinent to CAR-T utilization in the rapidly evolving landscape of R/R ALL management. These include optimal sequencing of CAR-T among other novel immunotherapeutic agents, the role of consolidation and maintenance following CAR-T, novel CAR-T constructs currently under clinical development, and strategies to optimize use of commercially available CAR-T products to improve patient outcomes.

When You Get to the Fork in the Road, Take It: The Challenges in Managing Patients With Advanced Prostate Cancer.

As is the case with most solid tumors, the heterogeneity of the disease biology of prostate cancer presents clinicians managing this disease with daily challenges. However, in contrast to other common cancers such as breast, lung, and colorectal cancers, there are unique challenges in prostate cancer management, including the variety of clinicians who manage aspects of the disease (urologists, medical oncologist, radiation oncologists) and the striking absence of prospective comparative data to inform the optimal sequence of systemic therapy in patients with metastatic castration-resistant disease. The purpose of this review is to attempt to assist practicing oncologists with sorting through the myriad of prostate cancer disease subsets and the challenges in making therapeutic decisions in multiple data-free zones given the absence of level 1 comparative clinical trials in the metastatic hormone-sensitive and castration-resistant states.

Failure analysis and life extension of mechanical products under multiple service conditions

Abstract During the service of engineering machinery products, the interaction of mechanical load, service environment and other conditions leads to the diversity and uncertainty of failure modes. Mechanism analysis is difficult to accurately calculate the dynamic changes of performance. Most existing machine learning methods only focus on the remaining useful life, without considering the internal mechanism and connection of multiple failure modes. In this paper, a resistance degradation model is established based on the data of multiple service cycles. According to the service data under different working conditions, a dynamic resistance degradation model based on classification and regression tree (CART) and a product service life enhancement strategy based on working condition sequence optimization are proposed. Firstly, the characteristics of different working conditions are analysed. Secondly, the data of multiple sensors are fused, and CART is used to fit the degradation model. Finally, a product service life optimization problem is designed to increase the life. Experiments show that the proposed method can fit the resistance degradation process, and can significantly improve the life of mechanical products.

A Data-Driven Predictive Control Method for Modeling Doubly-Fed Variable-Speed Pumped Storage Units

In this study, a data-driven model predictive control (MPC) method is proposed for the optimal control of a doubly-fed variable-speed pumped storage unit. This method combines modern control theory with the dynamic characteristics of the pumped storage unit to establish an accurate dynamic model based on actual operating data. In each control cycle, the MPC uses the system model to predict future system behavior and determines the optimal control input sequence by solving the constrained optimization problem, thereby effectively dealing with the nonlinearity, time-varying characteristics, and multivariable coupling problems of the system. When compared with a traditional PID control, this method significantly improves control accuracy, response speed, and system stability. The simulation results show that the proposed MPC method exhibits better steady-state error, overshoot, adjustment time, and control energy under various operating conditions, demonstrating its advantages in complex multivariable systems. This study provides an innovative solution for the efficient control of doubly-fed variable-speed pumped storage units and lays a solid foundation for the efficient utilization of new energy sources.

Increasing resilience and selection of a strategy for restoring transport networks in extreme natural processes

Aim. The development of an approach to increasing resilience by selecting strategies for restoring transport networks affected by extreme natural processes. Methods. This study evaluates the dynamics of extreme natural processes, specifically exogenous geological processes that can that can disrupt transport networks. It includes as framework for assessing the sustainability and restoration of transport networks under climate risk factors. Strategies for restoring the transport network were formulated. Results. The formulated strategies enable network modeling of transport network topology, which can be represented as an undirected weighted graph with a set of nodes and edges. The proposed model allows determining the most effective strategy for quickly restoring the connectivity of the transport network by determining the optimal sequence of restoration for repairing road sections, considering restoration time. The efficiency of restoring damaged sections of the transport network is expected to decrease as the share of the restored network increases. Therefore, it is crucial to estimate the necessary extend of network restoration to perform the necessary extent of network restoration to support emergency and urgent tasks by RSChS formations in specific areas. Conclusion. The analysis and assessment of alternative solutions for restoring the sustainability of transport networks considers the complexity of tasks under climate risk factors, such as extreme natural processes. In some cases, the RSChS problems do not require complete network restoration, unlike the tasks solved by the transport industry. This work aims to develop a framework for assessing restoration strategies, identifying the features of each of the considered strategies under uncertainty, and increasing operational sustainability. The proposed approach is flexible, allowing decision makers to assess various priorities during a specific natural emergency in a certain area, such as average recovery time, process efficiency, and uncertainty levels, when choosing the most desirable strategy. It is assumed that the average recovery time does not differ significantly among strategies for full network restoration. However, for partial restorations necessary for RSChS tasks, the average restoration time depends on the chosen strategy.

A Numerical Investigation of the Influence of the Wake for Mixed Layout Wind Turbines in Wind Farms Using FLORIS

A common retrofitting method for wind farms is the replacement of low-power turbines with high-power ones. The determination of the optimal replacement sequence for the purpose of maximizing revenue is a significant challenge. This paper employs a combination of FLORIS and a sequencing algorithm to simulate the power output resulting from the replacement of 1.5 MW small turbines with 5 MW large turbines. This study demonstrates that the optimal strategy for maximizing the overall power output is to replace the turbines in the first column. When the turbines situated in the first column have already undergone replacement or are unable to be replaced due to the characteristics of the terrain, it would be prudent to prioritize those in the final column. In the case of staggered arrangements, priority should be given to diagonal points that do not have turbines situated behind them. In the case of replacing the same number of large wind turbines, the preferred replacement option has a minimal impact on the power output of the existing small wind turbines, with an estimated reduction of 0.67%. This effectively enhances the economic efficiency of wind farm renovation.

Differential-algebraic equation-based model predictive control for trajectory tracking of spacecraft-mounted continuum manipulators

Spacecraft-mounted continuum manipulators (SMCMs) exhibit great potential for performing dexterous operations in unstructured environments due to their inherent compliance and dexterity. However, the dynamic model of the rigid-flexible coupling SMCM is highly nonlinear and typically formulated as a set of implicit differential-algebraic equations (DAEs), posing significant challenges for precise trajectory tracking control. This paper proposes a novel model predictive control (MPC) framework specifically designed for generic DAEs to achieve precise trajectory tracking of the SMCM under uncertain disturbances and input limitations. The DAE model of the SMCM is discretized into a set of nonlinear algebraic equations. By performing implicit differentiation of these equations with respect to the system state, the state transition matrix (STM) for the DAE model is derived. The optimal control action for the SMCM can be further determined based on the derived STM. Additionally, nonlinear complementary functions are introduced to address the issue of input limitations, allowing the problem of determining the optimal control sequence to be equivalently transformed into a set of nonlinear algebraic equations for solving. Numerical simulations demonstrate that the proposed approach can achieve precise trajectory tracking of the SMCM while strictly adhering to input limitations.

NeoDesign: a computational tool for optimal selection of polyvalent neoantigen combinations.

Tumor polyvalent neoantigen mRNA vaccines are gaining prominence in immunotherapy. The design of sequences in vaccine development is crucial for enhancing both the immunogenicity and safety of vaccines. However, a major challenge lies in selecting the optimal sequences from the large pools generated by multiple peptide combinations and synonymous codons. We introduce NeoDesign, a computational tool designed to tackle the challenge of sequence design. NeoDesign comprises four modules: Library Construction, Optimal Path Filtering, Linker Addition, and λ-Evaluation. It aims to identify the optimal protein sequence for tumor polyvalent neoantigen vaccines by minimizing linker usage, avoiding unexpected neoantigens and functional domains, and simplifying the structure. It also provides a preference scheme to balance mRNA stability and protein expression when designing mRNA sequences for the optimal protein sequence. This tool can potentially improve the sequence design of tumor polyvalent neoantigen mRNA vaccines, thereby significantly advancing immunotherapy strategies. NeoDesign is freely available on https://github.com/HuangLab-Fudan/neoDesign and https://figshare.com/projects/NeoDesign/221704.

Research on rapid prediction method of laser cladding deposited layer state based on molten pool texture sequence

A texture sequence feature combining texture statistical characteristics with time series is proposed for rapid prediction of the state of laser cladding deposited layer. Texture features in the molten pool images are extracted using high-order dense texture descriptor operators and transformed into probability sequences with the aid of multi-cell histograms. Texture features are screened based on the types of points of interest to improve the sequence, enhancing the prediction accuracy and speed for deposited layer. For different types of points of interest, the optimal texture sequence obtained achieves an improvement of more than 6% in prediction accuracy for deposited layer state compared to the full-texture sequence. The proposed texture sequence features, when compared with molten pool image features, exhibit an average improvement in prediction speed of more than 17 times across different deep learning models. Overall, the results indicate that this method significantly improves prediction speed while maintaining accuracy, satisfying the requirements of high-speed monitoring in laser cladding.

Designing Optimal Probe Sequence for Breast Cancer Subtype Classification via Multiple MicroRNAs Recognition

Designing Optimal Probe Sequence for Breast Cancer Subtype Classification via Multiple MicroRNAs Recognition

Damage mechanism of proppant and conductivity reduction post fracturing in unconventional reservoirs

To investigate the impact of proppant embedding and crushing damage on the conductivity in unconventional oil and gas development, experiments on long-term conductivity under suspension injection of proppant were conducted on shale and tight sandstone samples. Based on elastic contact mechanics theory, finite element software (Abaqus) was secondarily developed to globally embed proppant meshes with cohesive elements, analyzing the dynamic changes involving proppant embedding, deformation, and crushing under different closure pressures. The results indicated that the conductivity of fractures is highly sensitive to closure pressure. When closure pressure increases to 40 MPa, the conductivity of shale damage exceeds 60 % compared to 10 MPa. Larger proppants and shale reservoirs accelerate the decline in conductivity significantly, compared to tight sandstone. Combined with laser scanning and fractal dimension identification, the roughness of shale fracture (JRC48) was found to be much greater than that of tight sandstone (JRC37), leading to significantly higher stress concentration between proppants and shale, resulting in a smaller crushing ratio. Additionally, the deformation of shale and contacting proppant is significantly greater than in sandstone, resulting in a rapid decline in shale oil production. These findings provide theoretical guidance for ensuring effective inflow near oil wells, necessitating optimization of proppant injection sequence and placement concentration.

An experimentally validated thermomechanical model for a parametric study on reducing residual stress in cast iron repair welding

Remarkable casting properties and superior mechanical characteristics of cast iron make it an ideal material for a wide range of industrial applications. However, the production of cast iron components may result in the formation of cracks and defects, posing a significant threat to their structural integrity. Repair welding is a promising solution to resolve cast iron production defects. However, repair welding cast iron components poses unique challenges that stem from residual stress (RS) formation and the possibility of cracking during the repair process. Moreover, research on cast iron repair is scarce. To overcome these challenges, this paper presents a thermo-mechanical model validated by experiments to reduce RS in cast iron repair welding through the optimization of welding parameters and weld sequences as well as the geometry of the repair area. An experimental bead-on-plate weld is set up in order to validate the developed thermo-mechanical model. The temperature distribution in the weld is measured using thermocouples placed around the weld line. An X-ray diffraction technique is used to measure the axial and transverse RS at different points around the weld line. The developed finite element model is employed to simulate the repair welding process and analyze the effect of inter-pass temperature, the number of welding passes, welding sequences, and groove geometry on the RS. The numerical approach applied in this study provides a framework for repair welding optimization of cast iron and other materials, fostering the development of more efficient and reliable repair methods for industrial applications.

Urban multi-scale ecological network sequence and spatial structure optimization: A case study in Nanjing city, China

Rapid urbanization has had a segmented effect on the ecological land and natural environment in cities. Constructing an ecological network is of vital importance to protect ecological resources and maintain regional ecological security. However, ecological spaces at different administrative scales have their own ecological functions and elemental characteristics. Therefore, we formed the ecological network sequence to present the integrative connection of multi-scale with municipal area (MA), main urban area (MUA) and central urban area (CUA) in Nanjing. Ecological sources were identified through “landscape—function—structure” framework, and ecosystem services value (ESV) was used as an auxiliary method to identify supplementary sources. Ecological corridors and some key points were identified through circuit theory. The results show that the area of ecological sources in MA, MUA and CUA was 427.3 km2, 62.5 km2 and 16.2 km2, respectively. By integrating sources of three scales, we got 14 sources (442.7 km2) in the first sequence and 10 sources (7.7 km2) in the second sequence. Apart from that, we also got 4 new supplementary sources with area of 13.5 km2. The length of ecological corridors in MA, MUA and CUA was 252.5 km, 263.9 km and 22.7 km, respectively. By integrating corridors of three scales, we found that the western corridors between the ecological sources had a higher gravitation. The ecological nodes are generally distributed in the landscape heterogeneity transition zone, ecological resource fringe zone and ecological spatial contiguous zone. The ecological pinch points are generally distributed along the water system. The ecological barrier points are generally distributed in the combination area among the urban block, village and road. In conclusion, this study made up for the research gap of finding the nesting and integration relationship among ecological networks at multi-scale.