Atomic simulations based on quantum mechanics (QM) calculations have entered into the tool box of chemists over the past few decades, facilitating an understanding of a wide range of chemistry ...problems, from structure characterization to reactivity determination. Due to the poor scaling and high computational cost intrinsic to QM calculations, one has to either sacrifice accuracy or time when performing large-scale atomic simulations. The battle to find a better compromise between accuracy and speed has been central to the development of new theoretical methods.The recent advances of machine-learning (ML)-based large-scale atomic simulations has shown great promise to the benefit of many branches of chemistry. Instead of solving the Schrödinger equation directly, ML-based simulations rely on a large data set of accurate potential energy surfaces (PESs) and complex numerical models to predict the total energy. These simulations feature both a high speed and a high accuracy for computing large systems. Due to the lack of a physical foundation in numerical models, ML models are often frustrated in their predictivity and robustness, which are key to applications. Focusing on these concerns, here we overview the recent advances in ML methodologies for atomic simulations on three key aspects. Namely, the generation of a representative data set, the extensity of ML models, and the continuity of data representation. While global optimization methods are the natural choice for building a representative data set, the stochastic surface walking method is shown to provide the desired PES sampling for both minima and transition regions on the PES. The current ML models generally utilize local geometrical descriptors as an input and consider the total energy as the sum of atomic energies. There are many flavors of data descriptors and ML models, but the applications for material and reaction predictions are still limited, not least because of the difficulty to train the associated vast global data sets. We show that our recently designed power-type structure descriptors together with a feed-forward neural network (NN) model are compatible with highly complex global PES data, which has led to a large family of global NN (G-NN) potentials.Two recent applications of G-NN potentials in material and reaction simulations are selected to illustrate how ML-based atomic simulations can help the discovery of new materials and reactions.
Advance care planning in Asian culture Cheng, Shao-Yi; Lin, Cheng-Pei; Chan, Helen Yue-lai ...
Japanese journal of clinical oncology,
09/2020, Letnik:
50, Številka:
9
Journal Article
Recenzirano
Odprti dostop
Abstract
Ageing has been recognized as one of the most critically important health-care issues worldwide. It is relevant to Asia, where the increasing number of older populations has drawn attention ...to the paramount need for health-care investment, particularly in end-of-life care. The advocacy of advance care planning is a mean to honor patient autonomy. Since most East Asian countries are influenced by Confucianism and the concept of ‘filial piety,’ patient autonomy is consequently subordinate to family values and physician authority. The dominance from family members and physicians during a patient’s end-of-life decision-making is recognized as a cultural feature in Asia. Physicians often disclose the patient’s poor prognosis and corresponding treatment options to the male, family member rather to the patient him/herself. In order to address this ethical and practical dilemma, the concept of ‘relational autonomy’ and the collectivism paradigm might be ideally used to assist Asian people, especially older adults, to share their preferences on future care and decision-making on certain clinical situations with their families and important others. In this review article, we invited experts in end-of-life care from Hong Kong, Indonesia, Japan, South Korea, Singapore and Taiwan to briefly report the current status of advance care planning in each country from policy, legal and clinical perspectives. According to the Asian experiences, we have seen different models of advance care planning implementation. The Asian Delphi Taskforce for advance care planning is currently undertaken by six Asian countries and a more detailed, culturally sensitive whitepaper will be published in the near future.
LASP: Fast global potential energy surface exploration Huang, Si‐Da; Shang, Cheng; Kang, Pei‐Lin ...
Wiley interdisciplinary reviews. Computational molecular science,
November/December 2019, 2019-11-00, 20191101, Letnik:
9, Številka:
6
Journal Article
Recenzirano
Here we introduce the LASP code, which is designed for large‐scale atomistic simulation of complex materials with neural network (NN) potential. The software architecture and functionalities of LASP ...will be overviewed. LASP features with the global neural network (G‐NN) potential that is generated by learning the first principles dataset of global PES from stochastic surface walking (SSW) global optimization. The combination of the SSW method with global NN potential facilitates greatly the PES exploration for a wide range of complex materials. Not limited to SSW‐NN global optimization, the software implements standard interfaces to dock with other energy/force evaluation packages and can also perform common tasks for computing PES properties, such as single‐ended and double‐ended transition state search, the molecular dynamics simulation with and without restraints. A few examples are given to illustrate the efficiency and capabilities of LASP code. Our ongoing efforts for code developing and G‐NN potential library building are also presented.
This article is categorized under:
Software > Simulation Methods
LASP is an atomistic simulation package targeted for solving the complex PES problems using the global neural network potentials.
Glucose pyrolysis, a model system in biomass utilization, is renowned for its great complexity, deep in reaction network hierarchy and rich in reaction patterns. The selectivity in glucose pyrolysis, ...e.g., the high yield of 5-hydroxymethylfurfural (HMF), a value-added platform product, remains an intriguing puzzle even after 60 years of experimental study. Here we resolve the whole reaction network of glucose pyrolysis using a global-to-global technique for reaction pathway sampling. This is achieved by establishing the first organic chemistry reaction database via stochastic surface walking (SSW) global optimization, building the global neural network (G-NN) potential via machine learning and extensively exploring the reaction network of glucose pyrolysis. In total, 6407 elementary reactions, screened out from more than 150 000 reaction pairs in glucose pyrolysis, are collected in our reaction database. The established reaction network from SSW-NN, further validated by first-principles calculations, reveals that for glucose to HMF, the lowest energy reaction pathway involves fructose and 3-deoxyglucos-2-ene (3-DGE) as key intermediates and a site-selective reaction type, retro-Michael-addition, for three consecutive dehydration steps. The overall barrier is determined to be 1.91 eV, being at least 0.19 eV lower than all previously proposed mechanisms, which assumes direct β-H elimination dehydration. The lowest pathways to the other two major products, furfural (FF) and hydroxyacetaldehyde (HAA), are also discovered with a similar barrier 1.95 eV, which exhibit a competing nature by sharing the same key intermediate, 3-ketohexose. Since chemical reactions occurring in fast glucose pyrolysis are generally present in biomass chemistry, containing essentially all reaction patterns of C–H–O elements, the methodology designed and the results presented would help to advance reaction design and mechanistic modeling in renewable fuels from biomass.
•Characterization of aldehyde emissions in oil fumes under a near real-setup.•Stir frying has the lowest cooking temperature resulting in the lowest aldehyde emissions.•Cooking oils with low ...unsaturated fatty acid produce less aldehyde and less toxic species.
Cooking oil fumes (COFs) contain a mixture of chemicals. Of all chemicals, aldehydes draw a great attention since several of them are considered carcinogenic and formation of long-chain aldehydes is related to fatty acids in cooking oils. The objectives of this research were to compare aldehyde compositions and concentrations in COFs produced by different cooking oils, cooking methods, and food types and to suggest better cooking practices. This study compared aldehydes in COFs produced using four cooking oils (palm oil, rapeseed oil, sunflower oil, and soybean oil), three cooking methods (stir frying, pan frying, and deep frying), and two foods (potato and pork loin) in a typical kitchen. Results showed the highest total aldehyde emissions in cooking methods were produced by deep frying, followed by pan frying then by stir frying. Sunflower oil had the highest emissions of total aldehydes, regardless of cooking method and food type whereas rapeseed oil and palm oil had relatively lower emissions. This study suggests that using gentle cooking methods (e.g., stir frying) and using oils low in unsaturated fatty acids (e.g., palm oil or rapeseed oil) can reduce the production of aldehydes in COFs, especially long-chain aldehydes such as hexanal and t,t-2,4-DDE.
Abstract Background In this study, we tested the hypothesis that a combined adipose-derived mesenchymal stem cell (ADMSC) and ADMSC-derived exosome therapy protected rat kidney from acute ...ischemia–reperfusion (IR) injury (i.e., ligation both renal arteries for 1 h and reperfusion for 72 h prior to euthanization). Methods and Results Adult-male SD rats (n = 40) were equally categorized into group 1 (sham control), group 2(IR), group 3IR + exosome (100 μg), group 4 IR + ADMSC (1.2 × 106 cells), and group 5 (IR-exosome-ADMSC). All therapies were performed at 3 h after IR procedure from venous administration. By 72 h, the creatinine level and kidney injury score were lowest in group 1 and highest in group 2, significantly higher in group 3 than in groups 4 and 5, and significantly higher in group 4 than in group 5 (all P < 0.0001). The protein expression of inflammatory (TNF-α/NF-κB/IL-1β/MIF/PAI-1/Cox-2), oxidative-stress (NOX-1/NOX-2/oxidized protein), apoptotic (Bax/caspase-3/PARP), and fibrotic (Smad3/TGF-β) biomarkers showed an identical pattern, whereas the anti-apoptotic (Smad1/5, BMP-2) and angiogenesis (CD31/vWF/angiopoietin) biomarkers and mitochondrial cytochrome-C showed an opposite pattern of creatinine level among the five groups (all P < 0.001). The microscopic findings of glomerular-damage (WT-1), renal tubular-damage (KIM-1), DNA-damage (γ-H2AX), inflammation (MPO/MIF/CD68) exhibited an identical pattern, whereas the podocyte components (podocin/p-cadherin/synaptopodin) displayed a reversed pattern of creatinine level (all P < 0.0001). Conclusion Combined exosome-ADMSC therapy was superior to either one for protecting kidney from acute IR injury.
To address the challenge of assessing sedation status in critically ill patients in the intensive care unit (ICU), we aimed to develop a non-contact automatic classifier of agitation using artificial ...intelligence and deep learning.
We collected the video recordings of ICU patients and cut them into 30-second (30-s) and 2-second (2-s) segments. All of the segments were annotated with the status of agitation as "Attention" and "Non-attention". After transforming the video segments into movement quantification, we constructed the models of agitation classifiers with Threshold, Random Forest, and LSTM and evaluated their performances.
The video recording segmentation yielded 427 30-s and 6405 2-s segments from 61 patients for model construction. The LSTM model achieved remarkable accuracy (ACC 0.92, AUC 0.91), outperforming other methods.
Our study proposes an advanced monitoring system combining LSTM and image processing to ensure mild patient sedation in ICU care. LSTM proves to be the optimal choice for accurate monitoring. Future efforts should prioritize expanding data collection and enhancing system integration for practical application.
Boron crystals, despite their simple composition, must rank top for complexity: even the atomic structure of the ground state of β-B remains uncertain after 60 years' study. This makes it difficult ...to understand the many exotic photoelectric properties of boron. The presence of self-doping atoms in the crystal interstitial sites forms an astronomical configurational space, making the determination of the real configuration virtually impossible using current techniques. Here, by combining machine learning with the latest stochastic surface walking (SSW) global optimization, we explore for the first time the potential energy surface of β-B, revealing 15 293 distinct configurations out of the 2 × 10
minima visited, and reveal the key rules governing the filling of the interstitial sites. This advance is only allowed by the construction of an accurate and efficient neural network (NN) potential using a new series of structural descriptors that can sensitively discriminate the complex boron bonding environment. We show that, in contrast to the conventional views on the numerous energy-degenerate configurations, only 40 minima of β-B are identified to be within 7 meV per atom in energy above the global minimum of β-B, most of them having been discovered for the first time. These low energy structures are classified into three types of skeletons and six patterns of doping configurations, with a clear preference for a few characteristic interstitial sites. The observed β-B and its properties are influenced strongly by a particular doping site, the B19 site that neighbors the B18 site, which has an exceptionally large vibrational entropy. The configuration with this B19 occupancy, which ranks only 15
at 0 K, turns out to be dominant at high temperatures. Our results highlight the novel SSW-NN architecture as the leading problem solver for complex material phenomena, which would then expedite substantially the building of a material genome database.
This study collected different probiotic isolates from animal and plant sources to evaluate the bile-salt hydrolase activity of probiotics in vitro. The deconjugation potential of bile acid was ...determined using high-performance liquid chromatography. HepG2 cells were cultured with probiotic strains with high BSH activity. The triglyceride (TG) and apolipoprotein B (apo B) secretion by HepG2 cells were evaluated. Our results show that the BSH activity and bile-acid deconjugation abilities of Pediococcus acidilactici NBHK002, Bifidobacterium adolescentis NBHK006, Lactobacillus rhamnosus NBHK007, and Lactobacillus acidophilus NBHK008 were higher than those of the other probiotic strains. The cholesterol concentration in cholesterol micelles was reduced within 24 h. NBHK007 reduced the TG secretion by 100% after 48 h of incubation. NBHK002, NBHK006, and NBHK007 could reduce apo B secretion by 33%, 38%, and 39%, respectively, after 24 h of incubation. The product PROBIO S-23 produced a greater decrease in the total concentration of cholesterol, low-density lipoprotein, TG, and thiobarbituric acid reactive substance in the serum or livers of hamsters with hypercholesterolemia compared with that of hamsters fed with a high-fat and high-cholesterol diet. These results show that the three probiotic strains of lactic acid bacteria are better candidates for reducing the risk of cardiovascular disease.
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