The differential effects of tumor burdens on predicting the net benefits of ssCART-19 cell treatment on r/r B-ALL patients

The tumor burden (TB) is significantly related to the severity of cytokine release syndrome (CRS) caused by CAR-T cells, but its correlation with therapeutic efficacy has not been systematically studied. This study focused on the effects of the TB level on both the safety and efficacy of ssCART-19 as a treatment for r/r B-ALL. Taking the 5% tumor burden as the boundary, the study participants were divided into 2 groups, high and low tumor burden groups. Under this grouping strategy, the impacts of differential r/r B-ALL TBs on the clinical therapeutic efficacy (CR rate and long-term survival) and safety profiles after ssCART-19 cell treatment were analysed. 78 patients were reported in this study. The differential B-ALL TBs significantly affected the complete remission (CR) rates of patients treated with ssCART-19, with rates of 93.94% and 75.56% in the low and high TB groups, respectively (P = 0.0358). The effects of TBs on long-term therapeutic efficacy were further studied based on event-free survival (EFS) and overall survival (OS) profiles; both the OS and EFS of the low TB group were better than those of the high TB group, but the differences were not statistically significant. Importantly, the time points of TB measurement did not significantly affect the OS and EFS profiles regardless of whether the TBs were measured before or after fludarabine-cyclophosphamide (FC) preconditional chemotherapy. On the other hand, the severity of CRS was significantly correlated with the TB level (P = 0.0080), and the incidence of sCRS was significantly related to the TB level (the sCRS incidence increased as the TB level increased, P = 0.0224). Unexpectedly, the ssCART-19 cell expansion peaks were not significantly different (P = 0.2951) between the study groups. Patients with a low r/r B-ALL TB yield more net benefits from CAR-T treatment than those with a high TB in terms of safety and CR rate. These findings are critical and valuable for determining the optimal CAR-T cell treatment window for r/r B-ALL patients and will further the development of comprehensive and reasonable CAR-T cell treatment plans for r/r B-ALL patients with differential TBs.Trial registration: ClinicalTrials.gov identifier, NCT03919240.

Resource Allocation in NOMA-Assisted Ambient Backscatter Communication System

This paper considers a non-orthogonal multiple access (NOMA)-assisted ambient backscatter communication (AmBC) system. To maximize the achievable sum rate (ASR) of the AmBC system, a joint optimization problem over a backscatter device (BD) grouping strategy, reflection coefficients, and decoding order is formulated, where the BD grouping strategy contains the number of BD groups and the BD allocation strategy. The BD grouping strategy, the reflection coefficients, and the decoding order are all intertwined, and the global search is extremely complex. As a result, we propose a four-step optimization algorithm. First, we give the closed-form optimal solution of the BD decoding order and reflection coefficient for a given grouping strategy. Then, for a given number of BD groups, we propose a low-complexity BD allocation strategy based on the complexity–performance trade-off. Finally, the number of BD groups with the largest ASR is selected as the global optimal number of BD groups. The simulation results show that the proposed four-step optimization algorithm is better than the benchmark solution.

Adaptive Grouping Brain Storm Optimization for Multimodal Optimization Problems

Multimodal optimization problem exists in multiple global and many local optimal solutions. The difficulty of solving these problems is finding as many local optimal peaks as possible on the premise of ensuring global optimal precision. This article presents adaptive grouping brainstorm optimization (AGBSO) for solving these problems. In this article, adaptive grouping strategy is proposed for achieving adaptive grouping without providing any prior knowledge by users. For enhancing the diversity and accuracy of the optimal algorithm, elite reservation strategy is proposed to put central particles into an elite pool, and peak detection strategy is proposed to delete particles far from optimal peaks in the elite pool. Finally, this article uses testing functions with different dimensions to compare the convergence, accuracy, and diversity of AGBSO with BSO. Experiments verify that AGBSO has great localization ability for local optimal solutions while ensuring the accuracy of the global optimal solutions.

BSO20: efficient brain storm optimization for real-parameter numerical optimization

Brain storm optimization (BSO) is an emerging global optimization algorithm. The primary idea is to divide the population into different clusters, and offspring are generated within a cluster or between two clusters. However, the problems of inefficient clustering strategy and insufficient exploration exist in BSO. In this paper, a novel and efficient BSO is proposed, called BSO20 (proposed in 2020). BSO20 pays attention to both the clustering strategy and the mutation strategy. First, we propose a hybrid clustering strategy, which combines two clustering strategies, i.e., nearest-better clustering and random grouping strategy. The size of the subpopulation clustered by two strategies is dynamically adjusted as the population evolves. Second, a modified mutation strategy is used in BSO20 to share information within a cluster or among multiple clusters to enhance the ability of exploration. BSO20 is tested on the problems of the 2017 IEEE Congress on Evolutionary Computation competition on real parameter numerical optimization. BSO20 is compared with several variants of BSO and two variants of particle swarm optimization, and the experimental results show that BSO20 is competitive.

Grouping theory of epidemiology

Based on the fact that there are groups of different trends(basic infection number R0), a grouping model of epidemiology and a grouping strategy for epidemic prevention are proposed.

Multimodal Data Guided Spatial Feature Fusion and Grouping Strategy for E-Commerce Commodity Demand Forecasting

E-commerce offers various merchandise for selling and purchasing with frequent transactions and commodity flows. An accurate prediction of customer needs and optimized allocation of goods is required for cost reduction. The existing solutions have significant errors and are unsuitable for addressing warehouse needs and allocation. That is why businesses cannot respond to customer demands promptly, as they need accurate and reliable demand forecasting. Therefore, this paper proposes spatial feature fusion and grouping strategies based on multimodal data and builds a neural network prediction model for e-commodity demand. The designed model extracts order sequence features, consumer emotional features, and facial value features from multimodal data from e-commerce products. Then, a bidirectional long short-term memory network- (BiLSTM-) based grouping strategy is proposed. The proposed strategy fully learns the contextual semantics of time series data while reducing the influence of other features on the group’s local features. The output features of multimodal data are highly spatially correlated, and this paper employs the spatial dimension fusion strategy for feature fusion. This strategy effectively obtains the deep spatial relations among multimodal data by integrating the features of each column in each group across spatial dimensions. Finally, the proposed model’s prediction effect is tested using e-commerce dataset. The experimental results demonstrate the proposed algorithm’s effectiveness and superiority.

Group Formation in Shared Spaces

In shared spaces, grouped pedestrians can gain dominance and thus get the right of way from vehicles more easily; grouping can make traffic planning less complicated, e.g. it reduces the number of agents that need to be considered while traffic planning. However, grouping is not well investigated in shared spaces given the dynamic environment and interactions in mixed traffic. In this paper, we apply a dynamic facility location algorithm based on appearance time, origin, and destination of road users before crossing a junction to explore an appropriate grouping strategy in shared spaces, in order to improve the safety and efficiency of traffic.

Compact Sine Cosine Algorithm with Multigroup and Multistrategy for Dispatching System of Public Transit Vehicles

This paper studies the problem of intelligence optimization, a fundamental problem in analyzing the optimal solution in a wide spectrum of applications such as transportation and wireless sensor network (WSN). To achieve better optimization capability, we propose a multigroup Multistrategy Compact Sine Cosine Algorithm (MCSCA) by using the compact strategy and grouping strategy, which makes the initialized randomly generated value no longer an individual in the population and avoids falling into the local optimum. New evolution formulas are proposed for the intergroup communication strategy. Performance studies on the CEC2013 benchmark demonstrate the effectiveness of our new approach regarding convergence speed and accuracy. Finally, we apply MCSCA to solve the dispatch system of public transit vehicles. Experimental results show that MCSCA can achieve better optimization.