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Effects of harvest number on the yield and quality of different alfalfa varieties under hydroponic conditions
Five alfalfa varieties, namely Zhongcao No.13, WL440HQ, WL525HQ, WL903, and WL712, were subjected to a hydroponic cultivation experiment under controlled conditions of 25℃ room temperature, a nutrient solution concentration of 1.5 times the standard Hoagland formulation, and a 10-hour photoperiod. The plants were harvested every 30 days for six consecutive harvests, and the effects of harvest time on the yield and quality of different alfalfa varieties were observed. Principal component and membership function analyses revealed that the acid detergent fiber (ADF), neutral detergent fiber (NDF), relative feeding value (RFV), and yield of WL903 ranged from 22.77% to 25.75%, 28.31% to 32.42%, 197.46 to 233.84 (unitless), and 0.41 kg·m -2 to 0.59 kg·m -2 , respectively. The membership function analysis of the five alfalfa varieties indicated that the ranking based on D-value was WL903 > WL440HQ > WL712 > WL525HQ > Zhongcao No.13. Compared with the other four varieties, this variety was superior in terms of yield and relative feeding value and could serve as a candidate for regional hydroponic alfalfa cultivation. However, it should be noted that this result is based on specific light and nutrient solution conditions, and further cost-effectiveness verification is needed for large-scale applications.
Deep-stromal implantation of a double-crosslinked bioengineered porcine collagen implant (BPCDX) in advanced keratoconus: a 12-month study of corneal remodeling and visual outcomes
Engineering a compact high-fidelity Staphylococcus aureus Cas9 variant with broader targeting range and mechanistic insights into its activation
Correction: The effects of viewing visual artwork on patients, staff, and visitors in healthcare settings: A scoping review
The ‘crazy rule-defying’ genes that determine sex
Experimental investigation on the effect of real environmental factors on photovoltaic module power output in Southern Algeria
Precision cardiovascular risk prediction in type 1 diabetes: An IMI2 SOPHIA analysis
Abstract Cardiovascular disease (CVD) is a major long-term complication and the leading cause of morbidity and mortality among individuals with type 1 diabetes (T1D), with a substantially higher prevalence compared to the general population and driven by multiple interrelated risk factors—underscoring the urgent need for accurate risk assessment. To support more tailored approaches to CVD prevention, recently, five discordant phenotypic risk profiles for CVD were identified in the general population in Europe with diverse relationship between body mass index and cardiometabolic biomarkers. Here, we show their applicability in 44,212 people with T1D. Improved glycemic control was linked to a decrease in CVD risk as people with T1D with lower glycated hemoglobin belonged to the baseline concordant cluster. This supports the contention that glycemic control in people with T1D is an integral part of lowering CVD risk.
Understanding AI adoption through expert discourse: A UTAUT-based analysis on LinkedIn
Technology experts shape, rather than follow, the trajectory of artificial intelligence (AI). Yet their collective voice on professional social networks has been largely unmapped. Drawing on tens of thousands of AI-related LinkedIn posts, this study marries an embedding-based topic-modeling pipeline with the Unified Theory of Acceptance and Use of Technology (UTAUT) to decode what those who build AI really value. Our findings move beyond a simple application of UTAUT to re-contextualize its core constructs for this expert population. We find that for these experts, adoption is a complex negotiation: Performance Expectancy (PE) is redefined as the potential for industry-wide transformative breakthroughs, Effort Expectancy (EE) evolves into a demand for cognitive efficiency, Social Influence (SI) becomes a dual role where experts both shape and are shaped by norms, and Facilitating Conditions (FCs) are viewed as a holistic ecosystem. Furthermore, our analysis shows how cultural context recalibrates each construct, underscoring that “one-size-fits-all” models misread global AI uptake. Beyond mapping discourse, this study delivers actionable foresight for leaders navigating the next wave of AI innovation. Based on the analysis of expert discourse, we proposed a re-contextualized UTAUT model for AI adoption, as illustrated in the conceptual model.
Tailor-made metasurfaces for scattering control
Rhenium isotopes reveal enhanced rock organic carbon oxidation over the Toarcian Oceanic Anoxic Event
Abstract Weathering plays a central role in the geological carbon cycle. Silicate mineral weathering is invoked as a stabilizing feedback on CO 2 emissions, for example from volcanism during the emplacement of Large Igneous Provinces. However, modern-day studies show weathering can emit CO 2 during oxidation of rock organic carbon (OC petro ) in sedimentary rocks and function as a positive feedback on climate warming. Here we measure the rhenium isotope composition (δ 187 Re) of Early Jurassic marine sediments to explore how OC petro oxidation rates changed during warming across the Toarcian Ocean Anoxic Event (T-OAE). We find a 0.22 ± 0.10‰ decrease in δ 187 Re values during the T-OAE, with mass balance modeling showing this can be explained by increased OC petro weathering intensity on land associated with 6–7 °C of global warming. We estimate this could have delivered 7600–20,490 PgC to the oceans and atmosphere, demonstrating that chemical weathering does not simply act as a stabilizing feedback during hyperthermal events.
Correction: Pilot study assessing gut microbial diversity among sexual and gender minority young adults
14 things our PhD supervisors got right and why it mattered
Exploring human-induced flood risks and sustainable urban resilience in Iwo Nigeria with focus on awareness drivers and mitigation pathways
Friction-differentiated separation of alkali ions through two-dimensional nanochannels based on niobate perovskite
An adaptive extended radial basis function based interval analysis method for structural engineering solutions
In engineering structures, uncertain interval analysis is a study hotspot. However, how to obtain the accurate and efficient upper and lower solution bounds of uncertainty in the uncertain interval analysis is one of the key problems at present. To address this issue, the paper proposes a new adaptive extended radial basis function based interval analysis method to obtain accurate and efficient structural bound solutions. Instead of the pre-selecting the width of the basis functions, an Extended Radial Basis Function (E-RBF) and the adjusted widths are combined to confirm each width of the Gaussian basis function. In order to obtain the widths of the Gaussian basis function, the adaptive scaling technique is introduced in the E-RBF model. Then six numerical functions are selected to evaluate the accuracy of the E-RBF method with adjusted widths. On the basis of ensuring the accuracy of the widths of the Gaussian basis function, the adaptive E-RBF based interval analysis method is constructed by using the E-RBF with adjusted widths, catching the critical points and establishing the adaptive procedure for solving the extreme certain interval bounds of complex structural engineering problems. Next, three structural numerical examples and one composite shell are produced to verify the validity and effectiveness of this adaptive E-RBF based interval analysis method.
Chaudhury S, Reshef R, Nikiforow S, et al. Subgroup analysis in pediatric patients from the phase 3 study of tabelecleucel for allogeneic or solid organ transplant recipients with Epstein–Barr virus-driven post-transplant lymphoproliferative disease after failure of rituximab or rituximab and chemotherapy (ALLELE). <i>Blood.</i> 2025;146(suppl 1):5488.
SHP2 suppressed the progression of IL-1β induced cartilage damage via modulating BRD4 associated autophagy and pyroptosis signaling pathway
Dew formation is associated with higher ecosystem productivity across diverse ecosystems
Multi-adaptive event-triggered consensus of positive multi-agent systems using pinning strategy
This paper investigates the multi-adaptive event-triggered consensus of positive multi-agent systems. First, two classes of event-triggered mechanisms are designed for leaders and followers, respectively. A multi-adaptive event-triggered pinning control protocol is proposed by virtue of the presented mechanisms. Compared to existing event-triggered pinning strategies, the proposed method further reduces communication costs and resource consumption. By using the matrix decomposition technique and linear programming approach, the gain matrices of the control protocol and sufficient conditions are constructed to ensure the positivity and consensus of the systems. The multi-adaptive event-triggered pinning control protocol is then extended to observer-based control scenarios, where pinning observers for both the leader and follower are designed separately, further reducing the update frequency of the observer. Moreover, the adaptive technique and event-triggered mechanism are combined to reduce the length of the triggering interval, thereby further conserving the overall system resources. Meanwhile, a bound for the minimum event-triggering interval is derived, which analytically proves the exclusion of Zeno behavior. Finally, the effectiveness of the results is verified via an illustrative example and comparative simulations.