High-dimensional Optimal Density Control with Wasserstein Metric Matching

We present a novel computational framework for density control in high-dimensional state spaces. The considered dynamical system consists of a large number of indistinguishable agents whose behaviors can be collectively modeled as a time-evolving probability distribution. The goal is to steer the agents from an initial distribution to reach (or approximate) a given target distribution within a fixed time horizon at minimum cost. To tackle this problem, we propose to model the drift as a nonlinear reduced-order model, such as a deep network, and enforce the matching to the target distribution at terminal time either strictly or approximately using the Wasserstein metric. The resulting saddle-point problem can be solved by an effective numerical algorithm that leverages the excellent representation power of deep networks and fast automatic differentiation for this challenging high-dimensional control problem. A variety of numerical experiments were conducted to demonstrate the performance of our method.Comment: 8 pages, 4 figures. Accepted for IEEE Conference on Decision and Control 202

Analysis of weighted co-regulatory networks in maize provides insights into new genes and regulatory mechanisms related to inositol phosphate metabolism

Sub-network 1 and 2 of “magenta2”, node annotation. (XLSX 10 kb

Warming-induced shifts in alpine soil microbiome: An ecosystem-scale study with environmental context-dependent insights

10 páginas.- 5 figuras.- referencias.- Supplementary data to this article can be found online at https://doi. org/10.1016/j.envres.2024.119206Climate warming is a pressing global issue with substantial impacts on soil health and function. However, the influence of environmental context on the responses of soil microorganisms to warming remains largely elusive, particularly in alpine ecosystems. This study examined the responses of the soil microbiome to in situ experimental warming across three elevations (3850 m, 4100 m, and 4250 m) in the meadow of Gongga Mountain, eastern Tibetan Plateau. Our findings demonstrate that soil microbial diversity is highly resilient to warming, with significant impacts observed only at specific elevations. Furthermore, the influence of warming on the composition of the soil microbial community is also elevation-dependent, underscoring the importance of local environmental context in shaping microbial evolution in alpine soils under climate warming. Notably, we identified soil moisture at 3850 m and carbon-to-nitrogen ratio at 4250 m as indirect predictors regulating the responses of microbial diversity to warming at specific elevations. These findings underscore the paramount importance of considering pre-existing environmental conditions in predicting the response of alpine soil microbiomes to climate warming. Our study provides novel insights into the intricate interactions between climate warming, soil microbiome, and environmental context in alpine ecosystems, illuminating the complex mechanisms governing soil microbial ecology in these fragile and sensitive environments.This study was funded by the Science and Technology Research Program of Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (IMHE-ZYTS-07), and the Youth Innovation Promotion Association, Chinese Academy of Sciences (2023391). M.D.B. acknowledges support from TED2021-130908B–C41/AEI/10.13039/501100011033/Unión European NextGenerationEU/PRTR and from the Spanish Ministry of Science and Innovation for the I + D + i project PID2020-115813RA-I00 funded by MCIN/AEI/10.13039/501100011033.Peer reviewe

Pretreatment Donors after Circulatory Death with Simvastatin Alleviates Liver Ischemia Reperfusion Injury through a KLF2-Dependent Mechanism in Rat

Objective. Severe hepatic ischemia reperfusion injury (IRI) can result in poor short- and long-term graft outcome after transplantation. The way to improve the viability of livers from donors after circulatory death (DCD) is currently limited. The aim of the present study was to explore the protective effect of simvastatin on DCD livers and investigate the underlying mechanism. Methods. 24 male rats randomly received simvastatin or its vehicle. 30 min later, rat livers were exposed to warm ischemia in situ for 30 min. Livers were removed and cold-stored in UW solution for 24 h, subsequently reperfused for 60 min with an isolated perfused rat liver system. Liver injury was evaluated during and after warm reperfusion. Results. Pretreatment of DCD donors with simvastatin significantly decreased IRI liver enzyme release, increased bile output and ATP, and ameliorated hepatic pathological changes. Simvastatin maintained the expression of KLF2 and its protective target genes (eNOS, TM, and HO-1), reduced oxidative stress, inhibited innate immune responses and inflammation, and increased the expression of Bcl-2/Bax to suppress hepatocyte apoptosis compared to DCD control group. Conclusion. Pretreatment of DCD donors with simvastatin improves DCD livers’ functional recovery probably through a KLF2-dependent mechanism. These data suggest that simvastatin may provide a potential benefit for clinical DCD liver transplantation

Boosting Electrocatalytic Nitrate-to-Ammonia Conversion via Plasma Enhanced CuCo Alloy–Substrate Interaction

Electrocatalytic conversion of widely distributed nitrate from industrial wastewater into value-added ammonia was proposed as an attractive and sustainable alternative to harvesting green ammonia. Herein, CuCo alloys were facilely synthesized for nitrate conversion, while nonthermal Ar-plasma was employed to enhance the adhesion strength between the electrocatalyst and substrate interface via regulating the surface hydrophobicity and roughness. Based on Ar-plasma treatment, a high ammonia yield rate (5129.29 μg cm-2 h-1) was achieved using Cu30Co70 electrocatalyst -0.47 V vs RHE, while nearly 100% of Faradaic efficiency was achieved using Cu50Co50 electrocatalyst at -0.27 V vs RHE (reversible hydrogen electrode). Validated by in situ spectroscopy and density functional theory calculations, the high activity of the CuCo alloy was derived from the regulation of Co to weaken the strong adsorption capacity of Cu and the shift of the d-band center to lower the energy barrier, while Ar-plasma modification promoted the formation of *NO to boost nitrate conversion

Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor

Classifying many-body quantum states with distinct properties and phases of matter is one of the most fundamental tasks in quantum many-body physics. However, due to the exponential complexity that emerges from the enormous numbers of interacting particles, classifying large-scale quantum states has been extremely challenging for classical approaches. Here, we propose a new approach called quantum neuronal sensing. Utilizing a 61 qubit superconducting quantum processor, we show that our scheme can efficiently classify two different types of many-body phenomena: namely the ergodic and localized phases of matter. Our quantum neuronal sensing process allows us to extract the necessary information coming from the statistical characteristics of the eigenspectrum to distinguish these phases of matter by measuring only one qubit. Our work demonstrates the feasibility and scalability of quantum neuronal sensing for near-term quantum processors and opens new avenues for exploring quantum many-body phenomena in larger-scale systems.Comment: 7 pages, 3 figures in the main text, and 13 pages, 13 figures, and 1 table in supplementary material