While trail running has seen the growing use of advanced footwear technologies (AFT), most scientific investigations have evaluated shoes in laboratory settings under non-fatigued conditions. This study compared the effect of two footwear foams on running economy and affective responses before and after a prolonged trail run, as well as their impact on performance. Fourteen trained male trail runners completed two sessions with shoes allocated in a randomized and counterbalanced order. The two shoes tested differed in their foam proprieties with the AFT foam being softer, more compliant and resilient than the traditional foam. Each session involved a 90-minute trail run performed at a clamped rate of perceived exertion of 14/20. Running economy, affective valence and arousal were assessed pre- and post-prolonged run. Performance was assessed as the distance completed during the task. No differences in running economy or affective responses were observed between the two footwear conditions before and after the prolonged run. No difference independent of timing were identified for affective valence and running economy, although a tendency for higher pleasure was reported with the AFT foam (p = 0.088). Similarly, no differences in distance covered were found between shoes conditions. However, exploratory analysis revelated an interaction effect between speed and footwear across time, with better speed preservation from the first to the last loop with the AFT foam. The prolonged trail run induced significant physiological (increased VO2, HR, RER) and affective (lower valence, increased arousal) changes, reflecting fatigue-induced shifts in metabolic and affective states. While the shoes did not induce changes in running economy in a field setting, they may offer benefits in terms of running experience and fatigue preservation.
7531 Background: In the absence of head-to-head trials comparing anti-BCMA×CD3 bispecific antibodies in TCE RRMM, this study used an unanchored matching-adjusted indirect comparison (MAIC) to compare the efficacy of linvoseltamab and elranatamab. Methods: Patient (pt)-level data from LINKER-MM1 (117 pts receiving linvoseltamab 200 mg, data cut-off [DCO] 7/2024, median follow-up [mFU] 21.3 months [mos]) and published data from MagnetisMM-3 Cohort A (123 elranatamab pts, DCO 9/2024, mFU 33.9 mos) were analyzed. Ten LINKER-MM1 pts with prior BCMA antibody–drug conjugate exposure were excluded to align with MagnetisMM-3. LINKER-MM1 pts were weighted to match MagnetisMM-3 pts on prespecified prognostic factors deemed most important by an international expert panel: cytogenetic risk, age, refractory status, R-ISS stage, ECOG PS, extramedullary and/or paramedullary disease. Objective response rate (ORR), very good partial response or better (≥VGPR) and complete response or better (≥CR) rates, duration of response (DOR), progression-free survival (PFS), and overall survival (OS) were compared. DOR and PFS in LINKER-MM1 were recalculated to match MagnetisMM-3 censoring rules. Additional MAICs matched all available prespecified prognostic factors, included all 117 LINKER-MM1 pts, or matched to a MagnetisMM-3 subgroup with ECOG PS 0/1. Results: After matching, linvoseltamab effective sample size (ESS) was 71.3 (range of patient weights: 0.04–2.90). Linvoseltamab demonstrated statistically significantly higher ORR and ≥CR rate, a numerically higher ≥VGPR rate, and longer DOR, PFS, and OS vs elranatamab (Table). The additional MAICs yielded directionally consistent findings. Conclusions: Linvoseltamab demonstrated significantly higher ORR and ≥CR rate, numerically better ≥ VGPR rate, DOR, PFS, and OS compared with elranatamab, though the follow-up was shorter. These results highlight the potential of linvoseltamab as a highly effective treatment option for TCE RRMM. Elranatamab Linvoseltamab Linvoseltamab Linvoseltamab vs elranatamab Linvoseltamab vs elranatamab N=123 Unadjusted N=107 Adjusted ESS= 71.3 Unadjusted Adjusted % % % OR (CI) OR (CI) ORR 61 71 71 1.57 (1.04–2.37)* 1.60 (1.00–2.57)* ≥VGPR 56 64 65 1.36 (0.93–2.00) 1.45 (0.94–2.24) ≥CR 37 52 50 1.84 (1.26–2.68)* 1.71 (1.12–2.61)* Median, mos (CI); 12-mo landmark % Median, mos (CI); 12-mo landmark % Median, mos (CI); 12-mo landmark % HR (CI) HR (CI) DOR NR (29.4–NE); 73.9 NR (NE–NE); 82.8 NR (NE–NE); 84.4 0.93 (0.53–1.66) 0.82 (0.43–1.55) PFS 17.2 (9.8–NE); 56.4 NR (15.7–NE); 65.5 NR (16.2–NE); 64.7 0.86 (0.59–1.27) 0.86 (0.55–1.34) OS 24.6 (13.4–NE); 62.3 31.4 (27.8–NE); 75.5 NR (27.8–NE); 74.6 0.70 (0.47–1.04) 0.67 (0.42–1.05) OR >1 or HR <1 favor linvoseltamab. *Statistically significant at p<0.05. CI: 95% confidence interval, HR: hazard ratio, NE: not estimable, NR: not reached, OR: odds ratio.
Increasingly advanced soil survey techniques urgently require higher efficiency of soil mapping. Color, as a primary visual variable, can effectively encode soil classes and their distributions over space. However, the complexity of the soil maps arising from the multiple levels and classes to be represented makes it challenging to select distinguishable colors for each soil class. To date, methods for automatedly selecting distinguishable colors that align with the hierarchical structure of soil taxonomy remain unknown. In this paper, we propose an automated color selection algorithm for soil maps to clearly represent similarities and differences between soil classes. First, we encode soil classes in multi-levels with three semantic relationships between map colors (i.e., differentiation, association, and sequence), quantifying the soil map's hierarchical denotative quality. Then, we formulate the soil map color selection as an optimization problem and adaptively generate colors that align with the hierarchical structure of soil taxonomy through heuristic search. To test our method, we conducted a map-reading experiment and a usability survey based on five test maps. The results of the map-reading experiment demonstrate that our method for colorizing soil maps significantly outperforms the expert-based method in terms of effectiveness and efficiency. Furthermore, the usability survey indicates that soil maps colorized using our approach are more favorable compared to those colorized according to the Chinese national standard.
Abstract Advanced footwear technology (AFT) has transformed competitive running, yet individual and sex-specific responses to different AFT models remain unclear, particularly near race pace. This study examined running economy (RE) and gait biomechanics in response to three top-tier AFT models (Shoe A: adidas Pro Evo 2; Shoe B: Nike Alphafly 3; Shoe C: On CloudBoom Strike 2) in 14 male and 12 female well-trained runners at sex-specific submaximal speeds (16 and 14 km·h⁻¹). RE, spatiotemporal, and joint kinematic/kinetic data were collected via indirect calorimetry, accelerometry, and three-dimensional motion capture with force platforms. RE was significantly lower in Shoe C than Shoe A (males: 2.1%; females: 1.4%) and Shoe B (males: 1.9%; females: 0.9%), with 73% of runners responding favourably to Shoe C, a more consistent response than previously reported. Despite being lightest, Shoe A produced the poorest RE, challenging conventional mass-economy assumptions. Biomechanically, Shoe C elicited greater impact magnitude, lower ankle quasi-stiffness, and greater ankle angular velocity during early stance. Female runners showed smaller RE improvements, potentially related to lower running velocity and body mass limiting midsole engagement. The most efficient AFT enabled these well-trained runners to be more spring-like through tolerating higher forces and faster angular velocities without greater demand on metabolic cost.
Choice behavior research accounts for temporal and spatial variables that mediate the relationship between environmental events and choice-making. As machine learning (ML) tools are increasingly utilized for behavioral data analysis, we evaluated the efficacy of various algorithms in retrodicting reinforcement contingencies from binary choice sequences. A single-neuron spiking neural network (S-SNN) model was recently used to retrodict which reinforcement contingencies were in effect during training, effectively performing a reverse inference from observed behavior to learning conditions. In this study, we assess the ability of various ML models to perform the same task: using nine 5-s windows of choice behavior following the delivery of one of nine reinforcers within seven components defined by concurrent variable-interval schedules sampled across 50 training sessions. We evaluated each model’s ability to infer the contingencies shaping choice behavior (i.e., learning histories). To enable direct comparisons with the prior S-SNN study, we used the same datasets. Our findings offer guidance for selecting ML tools suited to behavioral data and highlight the importance of modeling spatiotemporal structure when analyzing learning histories.
