Tracking ocean heat uptake during the surface warming hiatus.

Ocean heat uptake is observed to penetrate deep into the Atlantic and Southern Oceans during the recent hiatus of global warming. Here we show that the deep heat penetration in these two basins is not unique to the hiatus but is characteristic of anthropogenic warming and merely reflects the depth of the mean meridional overturning circulation in the basin. We find, however, that heat redistribution in the upper 350 m between the Pacific and Indian Oceans is closely tied to the surface warming hiatus. The Indian Ocean shows an anomalous warming below 50 m during hiatus events due to an enhanced heat transport by the Indonesian throughflow in response to the intensified trade winds in the equatorial Pacific. Thus, the Pacific and Indian Oceans are the key regions to track ocean heat uptake during the surface warming hiatus

To Measure In-plane conductivity of Nafion membrane with general electrochemical approach

It is important to measure in-plane conductivity of Nafion membrane for fuel cell, but this target is generally inhibited by measuring system with heterogeneous interfaces and immature electrochemical measurements. This paper simply used water media to establish stable measuring system with metal electrode and Nafion membrane, representing system as equivalent circuit. Our equivalent circuit was validated by both cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) measurements, also clarified connection and difference in two measurements. This electrochemistry breakthrough helps realize measuring system completely and reliably even under successive temperature cycles, providing circuit elements for kinetic analysis in contact resistance, in-plane conductivity, inactive and active proton. We also clarified that the inactive and active proton shift can dictate low frequency inductance, which is an important sign for active and stable operation in fuel cell, secondary battery and materials. All these results can induce enormous progress in multi-disciplines, making our work have great significance and broad impact for future studies

Schr\"odinger-Heisenberg Variational Quantum Algorithms

Recent breakthroughs have opened the possibility to intermediate-scale quantum computing with tens to hundreds of qubits, and shown the potential for solving classical challenging problems, such as in chemistry and condensed matter physics. However, the extremely high accuracy needed to surpass classical computers poses a critical demand to the circuit depth, which is severely limited by the non-negligible gate infidelity, currently around 0.1-1%. Here, by incorporating a virtual Heisenberg circuit, which acts effectively on the measurement observables, to a real shallow Schr\"odinger circuit, which is implemented realistically on the quantum hardware, we propose a paradigm of Schr\"odinger-Heisenberg variational quantum algorithms to resolve this problem. We choose a Clifford virtual circuit, whose effect on the Hamiltonian can be efficiently and classically implemented according to the Gottesman-Knill theorem. Yet, it greatly enlarges the state expressivity, realizing much larger unitary t-designs. Our method enables accurate quantum simulation and computation that otherwise is only achievable with much deeper and more accurate circuits conventionally. This has been verified in our numerical experiments for a better approximation of random states and a higher-fidelity solution to the ground state energy of the XXZ model. Together with effective quantum error mitigation, our work paves the way for realizing accurate quantum computing algorithms with near-term quantum devices.Comment: We propose a framework of virtual Heisenberg-circuits-enhanced variational quantum algorithms, which can noiselessly increase the effective circuit depth to enlarge the quantum circuit expressivity and find high-fidelity ground state

(E)-Methyl N′-(3,4,5-trimethoxy­benzyl­idene)hydrazinecarboxyl­ate

The mol­ecule of the title compound, C12H16N2O5, adopts a trans configuration with respect to the C=N double bond. The dihedral angle between the benzene and hydrazinecarboxylic acid methyl ester planes is 12.55 (7)°. The mol­ecules are linked into a chain along [001] by inter­molecular N—H⋯O hydrogen bonds, and the chains are cross-linked into a two-dimensional zigzag structure by C—H⋯O hydrogen bonds

From Indeterminacy to Determinacy: Augmenting Logical Reasoning Capabilities with Large Language Models

Recent advances in LLMs have revolutionized the landscape of reasoning tasks. To enhance the capabilities of LLMs to emulate human reasoning, prior works focus on modeling reasoning steps using specific thought structures like chains, trees, or graphs. However, LLM-based reasoning continues to encounter three challenges: 1) Selecting appropriate reasoning structures for various tasks; 2) Exploiting known conditions sufficiently and efficiently to deduce new insights; 3) Considering the impact of historical reasoning experience. To address these challenges, we propose DetermLR, a novel reasoning framework that formulates the reasoning process as a transformational journey from indeterminate premises to determinate ones. This process is marked by the incremental accumulation of determinate premises, making the conclusion progressively closer to clarity. DetermLR includes three essential components: 1) Premise identification: We categorize premises into two distinct types: determinate and indeterminate. This empowers LLMs to customize reasoning structures to match the specific task complexities. 2) Premise prioritization and exploration: We leverage quantitative measurements to assess the relevance of each premise to the target, prioritizing more relevant premises for exploring new insights. 3) Iterative process with reasoning memory: We introduce a reasoning memory module to automate storage and extraction of available premises and reasoning paths, preserving historical reasoning details for more accurate premise prioritization. Comprehensive experimental results show that DetermLR outperforms all baselines on four challenging logical reasoning tasks: LogiQA, ProofWriter, FOLIO, and LogicalDeduction. DetermLR can achieve better reasoning performance while requiring fewer visited states, highlighting its superior efficiency and effectiveness in tackling logical reasoning tasks.Comment: Code repo: https://github.com/XiaoMi/DetermL

DEC1 binding to the proximal promoter of CYP3A4 ascribes to the downregulation of CYP3A4 expression by IL-6 in primary human hepatocytes

In this study, we provided molecular evidences that interleukin-6 (IL-6) contributed to the decreased capacity of oxidative biotransformation in human liver by suppressing the expression of cytochrome P450 3A4 (CYP3A4). After human hepatocytes were treated with IL-6, differentially expressed in chondrocytes 1 (DEC1) expression rapidly increased, and subsequently, the CYP3A4 expression decreased continuously. Furthermore, the repression of CYP3A4 by IL-6 occurred after the increase of DEC1 in primary human hepatocytes. In HepG2 cells, knockdown of DEC1 increased the CYP3A4 expression and its enzymatic activity. In addition, it partially abolished the decreased CYP3A4 expression as well as its enzymatic activity induced by IL-6. Consistent with this, overexpression of DEC1 markedly reduced the CYP3A4 promoter activity and the CYP3A4 expression as well as its enzymatic activity. Using sequential truncation and site directed mutagenesis of CYP3A4 proximal promoter with DEC1 construct, we showed that DEC1 specifically bound to CCCTGC sequence in the proximal promoter of CYP3A4, which was validated by EMSA and ChIP assay. These findings suggest that the repression of CYP3A4 by IL-6 is achieved through increasing the DEC1 expression in human hepatocytes, the increased DEC1 binds to the CCCTGC sequence in the promoter of CYP3A4 to form CCCTGC–DEC1 complex, and the complex downregulates the CYP3A4 expression and its enzymatic activity

Quantitative analysis of reaction gases or exhaust using an online process mass spectrometer

Online quantitative analysis of reaction gases or exhaust in industrial production is of great significance to improve the production capacity and process.Anovel method is developed for the online quantitative analysis of reaction gases or exhaust using quantitative mathematical models combined with the linear regression algorithm of machine learning. After accurately estimating the component gases and their contents in the reaction gases or exhaust, a ratio matrix is constructed to separate the relevant overlapping peaks. The ratio and calibration standard gases are detected, filtered, normalized, and linearly regressed with an online process mass spectrometer to correct the ratio matrices and obtain the relative sensitivity matrices. A quantitative mathematical model can be established to obtain the content of each component of the reaction gases or exhaust in real time. The maximum quantification error and relative standard deviation of the method are within 0.3% and 1%, after online quantification of the representative yeast fermenter tail gas