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Over the last decade, the survival outcome of patients with multiple myeloma (MM) has been substantially improved with the emergence of novel therapeutic agents, such as proteasome inhibitors, immunomodulatory drugs, anti-CD38 monoclonal antibodies, selective inhibitors of nuclear export (SINEs), and T cell redirecting bispecific antibodies. However, MM remains an incurable neoplastic plasma cell disorder, and almost all MM patients inevitably relapse due to drug resistance. Encouragingly, B cell maturation antigen (BCMA)-targeted chimeric antigen receptor T (CAR-T) cell therapy has achieved impressive success in the treatment of relapsed/refractory (R/R) MM and brought new hopes for R/R MM patients in recent years. Due to antigen escape, the poor persistence of CAR-T cells, and the complicated tumor microenvironment, a significant population of MM patients still experience relapse after anti-BCMA CAR-T cell therapy. Additionally, the high manufacturing costs and time-consuming manufacturing processes caused by the personalized manufacturing procedures also limit the broad clinical application of CAR-T cell therapy. Therefore, in this review, we discuss current limitations of CAR-T cell therapy in MM, such as the resistance to CAR-T cell therapy and the limited accessibility of CAR-T cell therapy, and summarize some optimization strategies to overcome these challenges, including optimizing CAR structure, such as utilizing dual-targeted/multi-targeted CAR-T cells and armored CAR-T cells, optimizing manufacturing processes, combing CAR-T cell therapy with existing or emerging therapeutic approaches, and performing subsequent anti-myeloma therapy after CAR-T cell therapy as salvage therapy or maintenance/consolidation therapy.

The objective of this work is to identify the maximum absorbed power and optimal buoy geometry of a heaving axisymmetric point absorber for a given cost in different sea states. The cost of the wave energy converter is estimated as proportional to the displaced volume of the buoy, and the buoy geometry is described by the radius-to-draft ratio. A conservative wave-height-dependent motion constraint is introduced to prevent the buoy from jumping out of the free surface of waves. The constrained optimization problem is solved by a two-nested-loops method, within which a core fundamental optimization process employs the MATLAB function fmincon. Results show that the pretension of the mooring system should be as low as possible. Except for very small energy periods, the stiffness of both the power take-off and mooring system should also be as low as possible. A buoy with a small radius-to-draft ratio can absorb more power, but at the price of working in more energetic seas and oscillating at larger amplitudes. In addition, the method to choose the optimal buoy geometry at different sea states is provided.

Point absorbers are extensively employed in wave energy conversion. In this work, we studied the point absorber with the buoy of a vertical cylindrical shape. Wave power absorption is obtained through the relative motion between the buoy and an internal mass. Three power-absorption degrees of freedom are investigated, i.e., surge, heave, and pitch, together with the influence of wave compliance of the buoy. Results show that, to absorb more power, the internal mass should be as large as possible for power absorption in translational degrees of freedom, i.e., surge and heave. The total rotational inertia should be as large as possible and the center of mass should be as low as possible for power absorption in pitch. Wave compliance of the buoy slightly enhances the power absorption in surge, but significantly weakens the power absorption in pitch. Surge is the best degree of freedom for power absorption owing to the highest efficiency, indicated by the largest capture width ratio. The simple resistive control is found to be adequate for wave power absorption of the self-reacting point absorber.

Change detection (CD) is a crucial task in remote sensing (RS) to distinguish surface changes from bitemporal images. Recently, deep learning (DL) based methods have achieved remarkable success for CD. However, the existing methods lack robustness to various kinds of changes in RS images, which suffered from problems of feature misalignment and inefficient supervision. In this paper, a deeply supervised attentive high-resolution network (DSAHRNet) is proposed for remote sensing image change detection. First, we design a spatial-channel attention module to decode change information from bitemporal features. The attention module is able to model spatial-wise and channel-wise contexts. Second, to reduce feature misalignment, the extracted features are refined by stacked convolutional blocks in parallel. Finally, a novel deeply supervised module is introduced to generate more discriminative features. Extensive experimental results on three challenging benchmark datasets demonstrate that the proposed DSAHRNet outperforms other state-of-the-art methods, and achieves a great trade-off between performance and complexity.

This work aims to establish a numerical model to investigate the wave interaction induced by the motion of a submerged cylindrical wave energy converter. The results show that when the submerged cylinder is in forced sinusoidal heave motion, distinct hollows and humps are produced on the free surface. As the heave amplitude increased from 1 m to 1.8 m, the depth of the hollow increased by 454%, and the height of the hump increased by 370%. Along with strong nonlinear phenomena, the generation of up to the fourth harmonic on the free surface above the submerged body is found, and the highest amplitude of the second harmonic waves reached 68% of the primary frequency. This indicates that the energy distribution of the wave is decomposed and rebalanced, and some energy in the primary frequency accumulates towards higher harmonics. When the submerged cylinder is in forced sinusoidal surge motion, the free surface elevation decreases in a stepwise manner as the wave transitions from crest to trough. As the cylinder pitches, the elevation of the wave trough decreases by 5% compared to when the submerged cylinder remains static.

Intelligent connected vehicles (ICVs) face challenges in handling intensive onboard computational tasks due to limited computing capacity. Vehicular edge computing networks (VECNs) offer a promising solution by enabling ICVs to offload tasks to mobile edge computing (MEC), alleviating computational load. As transportation systems are dynamic, vehicular tasks and MEC capacities vary over time, making efficient task offloading and resource allocation crucial. We explored a vehicle–road collaborative edge computing network and formulated the task offloading scheduling and resource allocation problem to minimize the sum of time and energy costs. To address the mixed nature of discrete and continuous decision variables and reduce computational complexity, we propose a hybrid hierarchical deep reinforcement learning (HHDRL) algorithm, structured in two layers. The upper layer of HHDRL enhances the double deep Q-network (DDQN) with a self-attention mechanism to improve feature correlation learning and generates discrete actions (communication decisions), while the lower layer employs deep deterministic policy gradient (DDPG) to produce continuous actions (power control, task offloading, and resource allocation decision). This hybrid design enables efficient decomposition of complex action spaces and improves adaptability in dynamic environments. Results from numerical simulations reveal that HHDRL achieves a significant reduction in total computational cost relative to current benchmark algorithms. Furthermore, the robustness of HHDRL to varying environmental conditions was confirmed by uniformly designing random numbers within a specified range for certain simulation parameters.

Captive breeding has been explored in Chinese sturgeon (Acipenser sinensis) for species protection. However, gonad development from stage II to IV of cultured female broodstocks is a handicap. This study aimed to explore the physiological and metabolic changes during the ovary development from stage II to IV of female Chinese sturgeon and the related energy regulatory mechanism, which may be helpful to address the developmental obstacle. The results showed that the oocyte volume increased and the muscle lipid content decreased with the ovary development. Ovarian RNA levels of most genes related to lipid and amino acid metabolism were higher in stage II and III than in stage IV. Serum contents of differential metabolites in arginine, cysteine, methionine, purine, tyrosine, lysine, valine, leucine and isoleucine metabolism pathways peaked at stage III, while the contents of sarcosine, alanine and histidine, as well as most oxylipins derived from fatty acids peaked at stage IV. These results indicated the more active amino acids, lipid metabolism, and energy dynamics of fish body in response to the high energy input of ovary developing from stage II to III, and the importance of alanine, histidine, taurine, folate and oxylipins for fish with ovary at stage IV.

Both aquatic and terrestrial biodiversity information can be detected in riverine water environmental DNA (eDNA). However, the effectiveness of using riverine water eDNA to simultaneously monitor the riverine and terrestrial biodiversity information remains unidentified. Here, we proposed that the monitoring effectiveness could be approximated by the transportation effectiveness of land-to-river and upstream-to-downstream biodiversity information flows and described by three new indicators. Subsequently, we conducted a case study in a watershed on the Qinghai–Tibet Plateau. The results demonstrated that there was higher monitoring effectiveness on summer or autumn rainy days than in other seasons and weather conditions. The monitoring of the bacterial biodiversity information was more efficient than the monitoring of the eukaryotic biodiversity information. On summer rainy days, 43–76% of species information in riparian sites could be detected in adjacent riverine water eDNA samples, 92–99% of species information in riverine sites could be detected in a 1-km downstream eDNA sample, and half of dead bioinformation (the bioinformation labeling the biological material that lacked life activity and fertility) could be monitored 4–6 km downstream for eukaryotes and 13–19 km downstream for bacteria. The current study provided reference method and data for future monitoring projects design and for future monitoring results evaluation.

Counting people in crowd scenarios is extensively conducted in drone inspections, video surveillance, and public safety applications. Today, crowd count algorithms with supervised learning have improved significantly, but with a reliance on a large amount of manual annotation. However, in real world scenarios, different photo angles, exposures, location heights, complex backgrounds, and limited annotation data lead to supervised learning methods not working satisfactorily, plus many of them suffer from overfitting problems. To address the above issues, we focus on training synthetic crowd data and investigate how to transfer information to real-world datasets while reducing the need for manual annotation. CNN-based crowd-counting algorithms usually consist of feature extraction, density estimation, and count regression. To improve the domain adaptation in feature extraction, we propose an adaptive domain-invariant feature extracting module. Meanwhile, after taking inspiration from recent innovative meta-learning, we present a dynamic-β MAML algorithm to generate a density map in unseen novel scenes and render the density estimation model more universal. Finally, we use a counting map refiner to optimize the coarse density map transformation into a fine density map and then regress the crowd number. Extensive experiments show that our proposed domain adaptation- and model-generalization methods can effectively suppress domain gaps and produce elaborate density maps in cross-domain crowd-counting scenarios. We demonstrate that the proposals in our paper outperform current state-of-the-art techniques.

Identifying frontier interdisciplinary domains is essential for tracking scientific evolution and informing strategic research planning. This study proposes a comprehensive framework that integrates (1) semantic disciplinary classification using a large language model (GPT-3.5-Turbo), (2) quantitative metrics for interdisciplinarity (degree and integration strength) and frontierness (novelty, growth, and impact), and (3) trend prediction using time series models, including Transformer, LSTM, GRU, Random Forest, and Linear Regression. The framework systematically captures both structural and temporal dimensions of emerging research fields. Compared to conventional citation-based or topic modeling approaches, it enhances semantic precision, supports multi-label classification, and enables forward-looking forecasts. Empirical validation shows that the Transformer model achieved the highest predictive performance, outperforming other deep learning and baseline models. As an illustrative example, the framework was applied to synthetic biology, which demonstrated high interdisciplinarity, strong novelty, and growing academic influence. These results underscore the field’s strategic position as a frontier interdisciplinary domain. Beyond this case, the proposed framework is generalizable to other domains and provides a scalable, data-driven solution for dynamic monitoring of emerging interdisciplinary areas. It holds promise for applications in science and technology intelligence, research evaluation, and policy support.