Implications of pleiotropy: challenges and opportunities for mining Big Data in biomedicine

Pleiotropy arises when a locus influences multiple traits. Rich GWAS findings of various traits in the past decade reveal many examples of this phenomenon, suggesting the wide existence of pleiotropic effects. What underlies this phenomenon is the biological connection among seemingly unrelated traits/diseases. Characterizing the molecular mechanisms of pleiotropy not only helps to explain the relationship between diseases, but may also contribute to novel insights concerning the pathological mechanism of each specific disease, leading to better disease prevention, diagnosis and treatment. However, most pleiotropic effects remain elusive because their functional roles have not been systematically examined. A systematic investigation requires availability of qualified measurements at multilayered biological processes (e.g., transcription and translation).The rise of Big Data in biomedicine, such as high-quality multi-omics data, biomedical imaging data and electronic medical records of patients, offers us an unprecedented opportunity to investigate pleiotropy. There will be a great need of computationally efficient and statistically rigorous methods for integrative analysis of these Big Data in biomedicine. In this review, we outline many opportunities and challenges in methodology developments for systematic analysis of pleiotropy, and highlight its implications on disease prevention, diagnosis and treatment

Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances

This paper presents a safe learning-based eco-driving framework tailored for mixed traffic flows, which aims to optimize energy efficiency while guaranteeing safety during real-system operations. Even though reinforcement learning (RL) is capable of optimizing energy efficiency in intricate environments, it is challenged by safety requirements during the training process. The lack of safety guarantees is the other concern when deploying a trained policy in real-world application. Compared with RL, model predicted control (MPC) can handle constrained dynamics systems, ensuring safe driving. However, the major challenges lie in complicated eco-driving tasks and the presence of disturbances, which respectively challenge the MPC design and the satisfaction of constraints. To address these limitations, the proposed framework incorporates the tube-based enhanced MPC (RMPC) to ensure the safe execution of the RL policy under disturbances, thereby improving the control robustness. RL not only optimizes the energy efficiency of the connected and automated vehicle in mixed traffic but also handles more uncertain scenarios, in which the energy consumption of the human-driven vehicle and its diverse and stochastic driving behaviors are considered in the optimization framework. Simulation results demonstrate that the proposed algorithm, compared with RMPC technique, shows an average improvement of 10.88% in holistic energy efficiency, while compared with RL algorithm, it effectively prevents inter-vehicle collisions

Physics-Augmented Data-EnablEd Predictive Control for Eco-driving of Mixed Traffic Considering Diverse Human Behaviors

Data-driven cooperative control of connected and automated vehicles (CAVs) has gained extensive research interest as it can utilize collected data to generate control actions without relying on parametric system models that are generally challenging to obtain. Existing methods mainly focused on improving traffic safety and stability, while less emphasis has been placed on energy efficiency in the presence of uncertainties and diversities of human-driven vehicles (HDVs). In this paper, we employ a data-enabled predictive control (DeePC) scheme to address the eco-driving of mixed traffic flows with diverse behaviors of human drivers. Specifically, by incorporating the physical relationship of the studied system and the Hankel matrix update from the generalized behavior representation to a particular one, we develop a new Physics-Augmented Data-EnablEd Predictive Control (PA-DeePC) approach to handle human driver diversities. In particular, a power consumption term is added to the DeePC cost function to reduce the holistic energy consumption of both CAVs and HDVs. Simulation results demonstrate the effectiveness of our approach in accurately capturing random human driver behaviors and addressing the complex dynamics of mixed traffic flows, while ensuring driving safety and traffic efficiency. Furthermore, the proposed optimization framework achieves substantial reductions in energy consumption, i.e., average reductions of 4.83% and 9.16% when compared to the benchmark algorithms

The effect of cooling rate on the wear performance of a ZrCuAlAg bulk metallic glass

In the present work, the local atomic ordering and the wear performance of ZrCuAlAg bulk metallic glass (BMG) samples with different diameters have been studied using transmission electron microscopy (TEM) plus autocorrelation function analysis, and pin-on-disc dry sliding wear experiments. Differential scanning calorimetry and TEM studies show that smaller diameter BMG sample has higher free volume and less local atomic ordering. The wear experiments demonstrate that with the same chemical composition, the smaller BMG sample exhibits higher coefficient of friction, higher wear rate, and rougher worn surface than those of the larger ones. Compared with larger BMG sample, the faster cooling rate of the smaller sample results in looser atomic configuration with more free volume, which facilitates the formation of the shear bands, and thus leads to larger plasticity and lower wear resistance. The results provide more quantitative understanding on the relationship among the cooling rate, the local atomic ordering, and the wear performance of BMGs

Research on the Production of 28Si Isotope with SiH4 by Gas Diffusion Method

Stable isotopes are currently widely used in medical，biological，agricultural，environmental，industrial manufacturing，scientific research and other fields. There are three isotopes of silicon in nature，namely 28Si，29Si，and 30Si，with natural abundances of 92.22%，4.69%，and 3.09%，respectively. 28Si isotopes are mainly used in the semiconductor field and also have certain applications in quantum computing and metrology. Silicon crystals made from pure 28Si isotope with an abundance of over 99% have a perfect lattice structure，which can reduce phonon scattering，improve thermal conductivity, lower gate voltage，increase switching speed，and increase chip frequency. 28Si can be used to manufacture high-speed CPUs，high-power devices，high-performance sensors，and more. Different experimental studies have shown that using 28Si materials with an abundance of over 99.85% to prepare semiconductor components can increase their thermal conductivity by 10%-60% compared to Si materials of natural abundance at room temperature. High abundance 28Si is a key material for preparing long spin coherent time devices in the field of quantum information，which can remove interference from 29Si. Silicon quantum bit is a promising quantum computing platform with advantages such as long coherence time，small device size，and compatibility with industrial manufacturing technology. In addition，28Si isotope can also be applied in metrology to define the exact values of the Avogadro constant and kilogram units. With SiH4 as the medium，the separation of 28Si isotope was studied by gas diffusion method in this paper. At present，research on the separation of silicon isotopes using cryogenic distillation methods (SiH4，SiCl4，or SiH3CH3 systems)，gas centrifugation methods (SiF4 or SiHCl3)，chemical exchange methods (systems of SiF4 and different complexing agents)，laser methods (Si2F6，SiF4)，and electromagnetic methods (SiH4) has achieved certain results both domestically and internationally. However，industrial production of silicon isotopes has not yet achieved breakthroughs. The cryogenic distillation method for silicon isotopes has a smaller separation coefficient，while the gas centrifugation method has a lower efficiency in separating light gases. The laser separation method has a low yield and high cost，and its economic viability for industrial production is poor. Nowadays，low-cost and high-quality polymer organic membranes have been widely applied in industry. A high-speed maglev compressor used under negative pressure conditions can effectively compress SiH4 gas. Compared to other potential separation media，SiH4 has a relatively small molecular weight and a relatively large gas diffusion separation coefficient. The overall separation factor of SiH4 can reach 1.010 measured through a 4-stage diffusion cascade experiment. Using multicomponent separation theory for cascade analysis and calculation，with natural SiH4 as raw material，the 28Si isotope abundance in light fractions can be concentrated to over 99% through a matched abundance ratio cascade (MARC) of no more than 300 stages. This study verifies the feasibility of diffusion separation of 28Si isotope with SiH4 as the medium

Research on geophysical response analysis and prediction technology of geostress in the shale gas area of the southern Sichuan Basin

The exploration and development potential of shale gas reservoirs in the Sichuan Basin is enormous; however, it also faces difficulties such as complex structures, strong heterogeneity, and unclear geophysical response characteristics. Fine prediction of geostress is an important part of shale gas exploration and development, which directly affects the implementation effect of reservoir evaluation, well trajectory design, and fracture reconstruction. The existing geostress prediction techniques lack high-precision seismic data constraints, making it difficult to accurately reflect the planar distribution characteristics of geostress in the block with rapid changes in complex tectonic zones. At the same time, the geophysical response characteristics of geostress in the Sichuan Basin are unknown, and the geostress seismic prediction technology lacks theoretical basis. This paper combines numerical simulation and physical experiments and defines the characteristics of the geophysical response of shale gas reservoirs in the Sichuan Basin changing with the stress field, and technical countermeasures for geostress seismic prediction have been established to provide technical means for accurate prediction of the geostress field in the shale gas block. Based on the geostress sensitive parameters obtained from prestack seismic inversion, the geostress field prediction of a shale gas work area in the Sichuan Basin is realized

Recombinant Goat VEGF164 Increases Hair Growth by Painting Process on the Skin of Shaved Mouse

To detect goat vascular endothelial growth factor (VEGF)-mediated regrowth of hair, full-length VEGF164 cDNA was cloned from Inner Mongolia cashmere goat (Capra hircus) into the pET-his prokaryotic expression vector, and the recombinant plasmid was transferred into E. coli BL21 cells. The expression of recombinant 6×his-gVEGF164 protein was induced by 0.5 mM isopropyl thio-β-D-galactoside at 32°C. Recombinant goat VEGF164 (rgVEGF164) was purified and identi ed by western blot using monoclonal anti-his and anti-VEGF antibodies. The rgVEGF164 was smeared onto the dorsal area of a shaved mouse, and we noted that hair regrowth in this area was faster than in the control group. Thus, rgVEGF164 increases hair growth in mice

Transmembrane Inhibitor of RICTOR/mTORC2 in Hematopoietic Progenitors

Summary Central to cellular proliferative, survival, and metabolic responses is the serine/threonine kinase mTOR, which is activated in many human cancers. mTOR is present in distinct complexes that are either modulated by AKT (mTORC1) or are upstream and regulatory of it (mTORC2). Governance of mTORC2 activity is poorly understood. Here, we report a transmembrane molecule in hematopoietic progenitor cells that physically interacts with and inhibits RICTOR, an essential component of mTORC2. Upstream of mTORC2 (UT2) negatively regulates mTORC2 enzymatic activity, reducing AKTS473, PKCα, and NDRG1 phosphorylation and increasing FOXO transcriptional activity in an mTORC2-dependent manner. Modulating UT2 levels altered animal survival in a T cell acute lymphoid leukemia (T-ALL) model that is known to be mTORC2 sensitive. These studies identify an inhibitory component upstream of mTORC2 in hematopoietic cells that can reduce mortality from NOTCH-induced T-ALL. A transmembrane inhibitor of mTORC2 may provide an attractive target to affect this critical cell regulatory pathway

Cell-State-Specific Metabolic Dependency in Hematopoiesis and Leukemogenesis

The balance between oxidative and non-oxidative glucose metabolism is essential for a number of pathophysiological processes. By deleting enzymes that affect aerobic glycolysis with different potencies, we examine how modulating glucose metabolism specifically affects hematopoietic and leukemic cell populations. We find that deficiency in the M2 pyruvate kinase isoform (PKM2) reduces levels of metabolic intermediates important for biosynthesis and impairs progenitor function without perturbing hematopoietic stem cells (HSC), whereas lactate dehydrogenase-A (LDHA) deletion significantly inhibits the function of both HSC and progenitors during hematopoiesis. In contrast, leukemia initiation by transforming alleles putatively affecting either HSC or progenitors is inhibited in the absence of either PKM2 or LDHA, indicating that the cell state-specific responses to metabolic manipulation in hematopoiesis do not apply to the setting of leukemia. This finding suggests that fine-tuning the level of glycolysis may be therapeutically explored for treating leukemia while preserving HSC function.National Institutes of Health (U.S.) (Grants P30CA147882 and R01CA168653)Smith Family FoundationBurroughs Wellcome FundVirginia and D.K. Ludwig Fund for Cancer ResearchDamon Runyon Cancer Research Foundatio