M2^2Hub: Unlocking the Potential of Machine Learning for Materials Discovery

We introduce M$^2$Hub, a toolkit for advancing machine learning in materials discovery. Machine learning has achieved remarkable progress in modeling molecular structures, especially biomolecules for drug discovery. However, the development of machine learning approaches for modeling materials structures lag behind, which is partly due to the lack of an integrated platform that enables access to diverse tasks for materials discovery. To bridge this gap, M$^2$Hub will enable easy access to materials discovery tasks, datasets, machine learning methods, evaluations, and benchmark results that cover the entire workflow. Specifically, the first release of M$^2$Hub focuses on three key stages in materials discovery: virtual screening, inverse design, and molecular simulation, including 9 datasets that covers 6 types of materials with 56 tasks across 8 types of material properties. We further provide 2 synthetic datasets for the purpose of generative tasks on materials. In addition to random data splits, we also provide 3 additional data partitions to reflect the real-world materials discovery scenarios. State-of-the-art machine learning methods (including those are suitable for materials structures but never compared in the literature) are benchmarked on representative tasks. Our codes and library are publicly available at https://github.com/yuanqidu/M2Hub

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

Background: Previous cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes. Methods: We conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment. Results: Forty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference − 0.40 [95% CI − 0.71 to − 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference − 1.6% [95% CI − 4.3% to 1.2%]; P = 0.42) between groups. Conclusions: In this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness. Trial registration: ISRCTN, ISRCTN12233792. Registered November 20th, 2017

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial.

BackgroundPrevious cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.MethodsWe conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment.ResultsForty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference - 0.40 [95% CI - 0.71 to - 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference - 1.6% [95% CI - 4.3% to 1.2%]; P = 0.42) between groups.ConclusionsIn this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness.Trial registrationISRCTN, ISRCTN12233792 . Registered November 20th, 2017

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial (vol 26, 46, 2022)

BackgroundPrevious cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.MethodsWe conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment.ResultsForty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference - 0.40 [95% CI - 0.71 to - 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference - 1.6% [95% CI - 4.3% to 1.2%]; P = 0.42) between groups.ConclusionsIn this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness.Trial registrationISRCTN, ISRCTN12233792 . Registered November 20th, 2017

Hydration of iodine adsorbed on the Au(111) surface

The hydration of halogens has been widely researched because of its close relation with the water desalination and biochemical reactions. In this work, by a combination of scanning tunneling microscopy and X-ray photoelectron spectroscopy, we have explored the hydration process of iodine via the Eley-Rideal process on the Au(111) surface. Moreover, the hydration process of iodine with the presence of the NiPc self-assembled network as a template has also been investigated, where the stepwise hydration of iodine at room temperature can be visualized on Au(111)

Predicting peak load of bus routes with supply optimization and scaled Shepard interpolation: A newsvendor model

The peak load of a bus route is essential to service frequency determination. From the supply side, there exist ineffective predicted errors of peak load for the optimal number of trips. Whilst many studies were undertaken to model demand prediction and supply optimization separately, little evidence is provided about how the predicted results of peak load affect supply optimization. We propose a prediction model for the peak load of bus routes built upon the idea of newsvendor model, which explicitly combines demand prediction with supply optimization. A new cost-based indicator is devised built upon the practical implication of peak load on bus schedule. We further devise a scaled Shepard interpolation algorithm to resolve discontinuities in the probability distribution of prediction errors arising from the new indicator, while leveraging the potential efficacy of multi-source data by adding a novel quasi-attention mechanism (i.e., scaling feature space and parameter optimization). The real-world application showed that our method can achieve high stability and accuracy, and is more robust to predicted errors with higher capacity. Our method can also produce a larger number of better trip supply plans as compared to traditional methods, while presenting stronger explanatory power in prioritizing the relative contribution of influential factors to peak load prediction

Significant advancement in geological theories and new discoveries of natural gas in China since the 11th Five-Year Plan

The development of geologic theories and exploration findings of natural gas in China supplement each other. Since the 11th Five-Year Plan in 2006–2010, geologic theories of natural gas in China has achieved notable advancement in many aspects, of which, are mainly reflected in the following seven aspects. Among them, there are two research progresses in the basic geological theory enumerated as follows. (1) The formation mechanisms of three types of natural gas that have been studied broadly including highly evolved coal-based source rocks, crude oil pyrolysis gas, and biogas. The cracked gas mode of coal-based source rocks, whole process hydrocarbon-generating mode of humus-type organic matter, and continuous biogas generation mode have been thoroughly advanced. (2) The theory of genetic identification between crude oil pyrolysis gas and kerogen pyrolysis gas, aggregated crude oil pyrolysis gas and dispersed crude oil pyrolysis gas, organic and inorganic gases, coal-type gas and oil origin gas, has been enriched extensively. There are five theoretical advances in the field of hydrocarbon accumulation in large gas fields: (1) the theory of hydrocarbon accumulation in ancient carbonate rock, “five paleo-structures control accumulation”, has been proposed innovatively; (2) the accumulation theory of tight sandstone gas in craton basins, foreland basins, and rift basins have been well-established; (3) the accumulation mode of “three-micro conveying, near-source enrichment, and sustained preservation” for ultra-deep and weak deformation zones has been established; (4) the accumulation theory of volcanic gas reserves in rift basins with basic elements of hydrocarbon generation troughs has been established and improved; (5) lastly, the accumulation theory of offshore high-temperature, overpressure, and deepwater gas were methodically deepened. The development of geologic theories of natural gas has promoted many new exploration discoveries. The accumulation theory in ancient carbonate reservoirs paved the way for the exploration of Anyue gas field in the Sichuan Basin, the largest single reserve in China. The new understanding of tight sandstone gas accumulation in the foreland thrust belt helped the first gas field discovery in the size 1 × 1012 m3 in an ultra-deep layer in the Kuqa Depression. The accumulation theory of ultra-deep reef reservoirs has guided the exploration of Yuanba gas field, the deepest-buried reef gas field in China. The theory of offshore hydrocarbon accumulation has led to remarkable discoveries in the South China Sea. Some of the said discoveries are the Dongfang 13-2 gas field, the largest gas field in China located in self-supported areas, and the Lingshui 17-2 gas field, which is a hundred billion cubic meters in size and is located in the deepwater exploration field. Keywords: Natural gas, Basic geological theory, Mechanism of gas generation, Accumulation of large gas fields, New exploration finding

Real-Space Evidence of Rare Guanine Tautomer Induced by Water

Water is vital for life as a solvent. Specifically, it has been well established that DNA molecules are hydrated in vivo, and water has been found to be responsible for the presence of some noncanonical DNA base tautomers. Theoretical investigations have shown that the existence of water could significantly influence the relative stability of different DNA base tautomers, reduce the energy barrier of tautomeric conversions, and thus promote the formation of some rare base tautomers. In this work, we report the real-space experimental evidence of rare base tautomers. From the high-resolution scanning tunneling microscopy imaging, we surprisingly find the formation of the rare guanine tautomer, <i>i.e.</i>, G/(3H,7H) form, on the Au(111) surface by delicately introducing water into the system. The key to the formation of this rare tautomer is proposed to be the “water bridge” that largely reduces the energy barriers of intramolecular proton-transfer processes as revealed by extensive density functional theory calculations. The real-space experimental evidence and the proposed mechanism make a step forward toward the fundamental understanding of water-assisted base tautomerization processes

PIEZO channel protein naturally expressed in human breast cancer cell MDA-MB-231 as probed by atomic force microscopy

Mechanical stimuli drives many physiological processes through mechanically activated channels, and the recent discovery of PIEZO channel has generated great interests in its mechanotransduction. Many previous researches investigated PIEZO proteins by transcribing them in cells that originally have no response to mechanical stimulation, or by forming PIEZO-combined complexes in vitro, and few studied PIEZO protein’s natural characteristics in cells. In this study we show that MDA-MB-231, a malignant cell in human breast cancer cell line, expresses the mechanosensitive behavior of PIEZO in nature without extra treatment, and we report its characteristics in response to localized mechanical stimulation under an atomic force microscope, wherein a correlation between the force magnitude applied and the channel opening probability is observed. The results on PIEZO of MDA-MB-231 can help establish a basis of preventing and controlling of human breast cancer cell via mechanical forces