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Progressive T cell exhaustion and predominance of aging tissue associated macrophages with advancing disease stage in penile squamous cell carcinoma
Improvement of anchor structural unit in FLAC3D and its application to the 110 construction method
The stakeholder game mechanisms in land use change of Caohai National Nature Reserve
Individual and group level health factors influence social networks of dairy calves
Multi-scale parallel gated local feature transformer
Divergent effects of environmental concern and risk perception on pro-environmental intention: an international study across 17 countries
Structural and antigenic characterization of Babesia Bovis HAP2 domains
Abstract The tick-borne apicomplexan parasite Babesia bovis causes bovine babesiosis which leads to enormous food and economic losses around the world. The existing resources to manage this disease are limited and have pitfalls, therefore, introduction of new strategies is urgently needed. B. bovis reproduces sexually in the midgut of its tick vector. HAP2, a well conserved ancient protein, plays a crucial role in the gamete fusion of this parasite and is a strong candidate for developing transmission-blocking vaccines. We previously demonstrated that immunization of cattle with full size B. bovis HAP2 blocks transmission of the parasite by Rhipicephalus microplus. Understanding the conserved structural features and antigenicity of HAP2 protein and its domains will facilitate developing effective methods to control pathogen transmission. In this study, we analyzed and compared AlphaFold2-predicted 3D structure of B. bovis HAP2 with the well-characterized crystal structures of HAP2 of Chlamydomonas reinhardtii and Arabidopsis thaliana. The comparisons and structural analysis resulted in the definition of three domains’ sequences, fusion loops, and disulfide bonds in the B. bovis HAP2. In addition, recombinant versions of each three predicted HAP2 domains were recognized by antibodies from HAP2 immunized and transmission-protected cattle, confirming their antigenicity. Remarkably, domain II was highly recognized compared to the other two domains. This study introduces new directions in designing novel functional assays and improved vaccine design through targeting the HAP2 protein.
Driving factors of pro-environmental behavior among rural tourism destination residents-considering the moderating effect of environmental policies
Impact of straight slot impingement jets on heat transfer enhancement of TiO2/H2O nanofluid flow in a square channel: CFD analysis
A Triangular Frustrated Eu(II)–Organic Framework for Sub-Kelvin Magnetic Refrigeration
Leveraging graph neural networks and gate recurrent units for accurate and transparent prediction of baseball pitching speed
Evaluating augmented reality in physical education for dyslexic students from the perspectives of teachers and students
The landscape of renal protein S-acylation in mice with lipid-induced nephrotoxicity
The effect of video-based multimedia information before amniocentesis on pain, anxiety, and pregnancy outcomes
Elucidating the chrononutrition patterns and sleep quality among subfertile patients with different vitamin D levels
Construction and validation of a nomogram based on the log odds of positive lymph nodes to predict the prognosis of T1 gastric cancer
Efficient and assured reinforcement learning-based building HVAC control with heterogeneous expert-guided training
Abstract Building heating, ventilation, and air conditioning (HVAC) systems account for nearly half of building energy consumption and $$20\%$$ of total energy consumption in the US. Their operation is also crucial for ensuring the physical and mental health of building occupants. Compared with traditional model-based HVAC control methods, the recent model-free deep reinforcement learning (DRL) based methods have shown good performance while do not require the development of detailed and costly physical models. However, these model-free DRL approaches often suffer from long training time to reach a good performance, which is a major obstacle for their practical deployment. In this work, we present a systematic approach to accelerate online reinforcement learning for HVAC control by taking full advantage of the knowledge from domain experts in various forms. Specifically, the algorithm stages include learning expert functions from existing abstract physical models and from historical data via offline reinforcement learning, integrating the expert functions with rule-based guidelines, conducting training guided by the integrated expert function and performing policy initialization from distilled expert function. Moreover, to ensure that the learned DRL-based HVAC controller can effectively keep room temperature within the comfortable range for occupants, we design a runtime shielding framework to reduce the temperature violation rate and incorporate the learned controller into it. Experimental results demonstrate up to 8.8X speedup in DRL training from our approach over previous methods, with low temperature violation rate.