Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability.

Activation energy characterization of competing reactions is a costly but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a transition state search using the highest level of theory that can be afforded. However, recently, several groups have popularized the idea of predicting activation energies directly based on nothing more than the reactant and product graphs, a sufficiently complex neural network, and a broad enough data set. Here, we have revisited this task using the recently developed Reaction Graph Depth 1 (RGD1) transition state data set and several newly developed graph attention architectures. All of these new architectures achieve similar state-of-the-art results of ∼4 kcal/mol mean absolute error on withheld testing sets of reactions but poor performance on external testing sets composed of reactions with differing mechanisms, reaction molecularity, or reactant size distribution. Limited transferability is also shown to be shared by other contemporary graph to activation energy architectures through a series of case studies. We conclude that an array of standard graph architectures can already achieve results comparable to the irreducible error of available reaction data sets but that out-of-distribution performance remains poor.

Reductive Doping Inhibits the Formation of Isomerization-Derived Structural Defects in N-doped Poly(benzodifurandione) (n-PBDF).

Recently, solution-processable n-doped poly(benzodifurandione) (n-PBDF) has been made through in-situ oxidative polymerization and reductive doping, which exhibited exceptionally high electrical conductivities and optical transparency. The discovery of n-PBDF is considered a breakthrough in the field of organic semiconductors. In the initial report, the possibility of structural defect formation in n-PBDF was proposed, based on the observation of structural isomerization from (E)-2H,2'H-[3,3'-bibenzofuranylidene]-2,2'-dione (isoxindigo) to chromeno[4,3-c]chromene-5,11-dione (dibenzonaphthyrone) in the dimer model reactions. In this study, we present clear evidence that structural isomerization is inhibited during polymerization. We reveal that the dimer (BFD1) and the trimer (BFD2) can be reductively doped by several mechanisms, including hydride transfer, forming charge transfer complexes (CTC) or undergoing an integer charge transfer (ICT) with reactants available during polymerization. Once the hydride transfer adducts, the CTC, or the ICT product forms, structural isomerization can be effectively prevented even at elevated temperatures. Our findings provide a mechanistic understanding of why isomerization-derived structural defects are absent in n-PBDF backbone. It lays a solid foundation for the future development of n-PBDF as a benchmark polymer for organic electronics and beyond.

Δ2 machine learning for reaction property prediction.

The emergence of Δ-learning models, whereby machine learning (ML) is used to predict a correction to a low-level energy calculation, provides a versatile route to accelerate high-level energy evaluations at a given geometry. However, Δ-learning models are inapplicable to reaction properties like heats of reaction and activation energies that require both a high-level geometry and energy evaluation. Here, a Δ2-learning model is introduced that can predict high-level activation energies based on low-level critical-point geometries. The Δ2 model uses an atom-wise featurization typical of contemporary ML interatomic potentials (MLIPs) and is trained on a dataset of ∼167 000 reactions, using the GFN2-xTB energy and critical-point geometry as a low-level input and the B3LYP-D3/TZVP energy calculated at the B3LYP-D3/TZVP critical point as a high-level target. The excellent performance of the Δ2 model on unseen reactions demonstrates the surprising ease with which the model implicitly learns the geometric deviations between the low-level and high-level geometries that condition the activation energy prediction. The transferability of the Δ2 model is validated on several external testing sets where it shows near chemical accuracy, illustrating the benefits of combining ML models with readily available physical-based information from semi-empirical quantum chemistry calculations. Fine-tuning of the Δ2 model on a small number of Gaussian-4 calculations produced a 35% accuracy improvement over DFT activation energy predictions while retaining xTB-level cost. The Δ2 model approach proves to be an efficient strategy for accelerating chemical reaction characterization with minimal sacrifice in prediction accuracy.

Deep reaction network exploration of glucose pyrolysis.

Resolving the reaction networks associated with biomass pyrolysis is central to understanding product selectivity and aiding catalyst design to produce more valuable products. However, even the pyrolysis network of relatively simple [Formula: see text]-D-glucose remains unresolved due to its significant complexity in terms of the depth of the network and the number of major products. Here, a transition-state-guided reaction exploration has been performed that provides complete pathways to most significant experimental pyrolysis products of [Formula: see text]-D-glucose. The resulting reaction network involves over 31,000 reactions and transition states computed at the semiempirical quantum chemistry level and approximately 7,000 kinetically relevant reactions and transition states characterized with density function theory, comprising the largest reaction network reported for biomass pyrolysis. The exploration was conducted using graph-based rules to explore the reactivities of intermediates and an adaption of the Dijkstra algorithm to identify kinetically relevant intermediates. This simple exploration policy surprisingly (re)identified pathways to most major experimental pyrolysis products, many intermediates proposed by previous computational studies, and also identified new low-barrier reaction mechanisms that resolve outstanding discrepancies between reaction pathways and yields in isotope labeling experiments. This network also provides explanatory pathways for the high yield of hydroxymethylfurfural and the reaction pathway that contributes most to the formation of hydroxyacetaldehyde during glucose pyrolysis. Due to the limited domain knowledge required to generate this network, this approach should also be transferable to other complex reaction network prediction problems in biomass pyrolysis.

Comprehensive exploration of graphically defined reaction spaces.

Existing reaction transition state (TS) databases are comparatively small and lack chemical diversity. Here, this data gap has been addressed using the concept of a graphically-defined model reaction to comprehensively characterize a reaction space associated with C, H, O, and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, heat of reaction, reactant and product geometries, frequencies, and atom-mapping. For 33,032 reactions, more than one TS was discovered by conformational sampling, allowing conformational errors in TS prediction to be assessed. Data is supplied at the GFN2-xTB and B3LYP-D3/TZVP levels of theory. A subset of reactions were recalculated at the CCSD(T)-F12/cc-pVDZ-F12 and ωB97X-D2/def2-TZVP levels to establish relative errors. The resulting collection of reactions and properties are called the Reaction Graph Depth 1 (RGD1) dataset. RGD1 represents the largest and most chemically diverse TS dataset published to date and should find immediate use in developing novel machine learning models for predicting reaction properties.

Thermally Accessible Prebiotic Pathways for Forming Ribonucleic Acid and Protein Precursors from Aqueous Hydrogen Cyanide.

The search for prebiotic chemical pathways to biologically relevant molecules is a long-standing puzzle that has generated a menagerie of competing hypotheses with limited experimental prospects for falsification. However, the advent of computational network exploration methodologies has created the opportunity to compare the kinetic plausibility of various channels and even propose new pathways. Here, the space of organic molecules that can be formed within four polar or pericyclic reactions from water and hydrogen cyanide (HCN), two established prebiotic candidates for generating biological precursors, was comprehensively explored with a state-of-the-art exploration algorithm. A surprisingly diverse reactivity landscape was revealed within just a few steps of these simple molecules. Reaction pathways to several biologically relevant molecules were discovered involving lower activation energies and fewer reaction steps compared with recently proposed alternatives. Accounting for water-catalyzed reactions qualitatively affects the interpretation of the network kinetics. The case-study also highlights omissions of simpler and lower barrier reaction pathways to certain products by other algorithms that qualitatively affect the interpretation of HCN reactivity.

Program for education and enrichment of relational skills (PEERS) training for social skills and depressed mood intervention in young adult with depression: Study protocol for a randomized controlled trial.

Introduction: Depression is a common psychiatric disorder characterized by persistent low mood, reduced interest, and slowed thinking. Young adults are the main first-onset group for depression in all categories of the population. Program for education and enrichment of relational skills (PEERS) training, a program for the Education and Enrichment of Relational Skills, has been used in Europe and America for people with various types of social disorders with good results. A Chinese adaptation of the PEERS training program may be a new approach to help youth with depression return to society as soon as possible. This study aimed to construct and optimize a social skills training program for Chinese young adults with depression and to validate the impact of the program. Materials and methods and analysis: The aim of this trial protocol is to evaluate the efficacy of the localized PEERS training program on social competence, depressed mood in a Chinese young adult population with depression. The primary outcome will be a change in self-reported depressive symptoms from baseline to week 3 post-randomization to week 6 post-randomization measured using the Liebowitz social anxiety scale (LSAS). Secondary outcomes include the rate of decline in severe social anxiety, the Social Avoidance and Distress Scale (SAD), the Social Self-Efficacy Scale (PSSE), and the Hamilton Depression Scale (HAMD-17). Data for each assessment will be collected at baseline, week 3 of the trial, and week 6 of the trial. Ethics and dissemination: Ethics approval was obtained from the Hospital Ethics Committee. Findings will be disseminated through scientific journals, conferences, and university courses. Trial registration number: [http://www.chictr.org.cn/], identifier [ChiCTR2100046050].

Algorithmic Explorations of Unimolecular and Bimolecular Reaction Spaces.

Algorithmic reaction exploration based on transition state searches has already made inroads into many niche applications, but its potential as a general-purpose tool is still largely unrealized. Computational cost and the absence of benchmark problems involving larger molecules remain obstacles to further progress. Here an ultra-low cost exploration algorithm is implemented and used to explore the reactivity of unimolecular and bimolecular reactants, comprising a total of 581 reactions involving 51 distinct reactants. The algorithm discovers all established reaction pathways, where such comparisons are possible, while also revealing a much richer reactivity landscape, including lower barrier reaction pathways and a strong dependence of reaction conformation in the apparent barriers of the reported reactions. The diversity of these benchmarks illustrate that reaction exploration algorithms are approaching general-purpose capability.

Deep reaction network exploration at a heterogeneous catalytic interface.

Characterizing the reaction energies and barriers of reaction networks is central to catalyst development. However, heterogeneous catalytic surfaces pose several unique challenges to automatic reaction network characterization, including large sizes and open-ended reactant sets, that make ad hoc network construction the current state-of-the-art. Here, we show how automated network exploration algorithms can be adapted to the constraints of heterogeneous systems using ethylene oligomerization on silica-supported single-site Ga3+ as a model system. Using only graph-based rules for exploring the network and elementary constraints based on activation energy and size for identifying network terminations, a comprehensive reaction network is generated and validated against standard methods. The algorithm (re)discovers the Ga-alkyl-centered Cossee-Arlman mechanism that is hypothesized to drive major product formation while also predicting several new pathways for producing alkanes and coke precursors. These results demonstrate that automated reaction exploration algorithms are rapidly maturing towards general purpose capability for exploratory catalytic applications.

Conformational Sampling for Transition State Searches on a Computational Budget.

Transition state searches are the basis for computationally characterizing reaction mechanisms, making them a pivotal tool in myriad chemical applications. Nevertheless, common search algorithms are sensitive to reaction conformations, and the conformational spaces of even medium-sized reacting systems are too complex to explore with brute force. Here, we show that it is possible to train a classifier to learn the features of reaction conformers that conduce successful transition state searches, such that optimal conformers can be down-selected before incurring the cost of a high-level transition state search. The efficacy and transferability of this approach were tested using four distinct benchmarks comprising over three hundred individual reactions. Neglecting conformer contributions led to qualitatively incorrect activation energy estimations for a broad range of reactions, whereas simple random forest classifiers reliably down-selected low-barrier reaction conformers for unseen reactions. The robust performance of these machine learning classifiers mitigates cost as a factor when implementing conformational sampling into contemporary reaction prediction workflows and opens up many avenues for further improvements as transition state data grow.

An enhanced photo-electrochemical sensor constructed from pillar [5]arene functionalized Au NPs for ultrasensitive detection of caffeic acid.

Pillar [5]arene, a new water-soluble carboxylatopillar [5]arene ammonium salts (WP5), has been employed as the host for complexation of guest molecules. Herein, a visible light driven WP5 functionalized gold nanoparticles (Au@WP5) was fabricated for ultrasensitive photelectrochemical (PEC) detection of caffeic acid (CA). The ultraviolet-visible spectrum characteristics, PEC response results of samples in caffeic acid solution confirm the localized surface plasmon resonance effect of Au NPs and the host-guest interaction between WP5 and CA are responsible for the enhanced PEC sensing performance. Under optimal conditions, the sensitive PEC sensor constructed with Au@WP5 exhibited the concentration linear range from 0.025 µM to 370 µM and a detection limit of 0.01 µM (S/N = 3). Importantly, the good anti-interference ability, stability and reproducibility of the proposed PEC sensor providing the promising detection application of pillar [5]arene functionalized photoactive materials in food and drinks.

MOF-derived RuCoP nanoparticles-embedded nitrogen-doped polyhedron carbon composite for enhanced water splitting in alkaline media.

Water splitting is considered as a promising candidate for renewable and sustainable energy systems, while developing efficient, inexpensive and robust bifunctional electrocatalysts for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) still remains a challenge. Herein, the well-designed RuCoP nanoparticles embedded in nitrogen-doped polyhedron carbon (RuCoP@CN) composite is fabricated by in-situ carbonization of Co based zeolitic imidazolate framework (ZIF-67) and phosphorization. Ru-substituted phosphate is proved to be imperative for the electrochemical activity and stability of individual catalysts, which can efficiently yield the active electronic states and promote the intrinsic OER and HER activity. As a result, a current density of 10 mA cm-2 is achieved at a cell voltage as low as 1.60 V when the RuCoP@CN electrocatalyst applied for the overall water splitting, which is superior to the reported RuO2 and Pt/C couple electrode (1.64 V). The density functional theory (DFT) calculations reveal that the introduction of Ru and P atoms increase the electronic states of Co d-orbital near the Fermi level, decreasing the free energy of the hydrogen adsorption and H2O dissociation for HER and the rate-limiting step for OER in alkaline media.

Topology Automated Force-Field Interactions (TAFFI): A Framework for Developing Transferable Force Fields.

Force-field development has undergone a revolution in the past decade with the proliferation of quantum chemistry based parametrizations and the introduction of machine learning approximations of the atomistic potential energy surface. Nevertheless, transferable force fields with broad coverage of organic chemical space remain necessary for applications in materials and chemical discovery where throughput, consistency, and computational cost are paramount. Here, we introduce a force-field development framework called Topology Automated Force-Field Interactions (TAFFI) for developing transferable force fields of varying complexity against an extensible database of quantum chemistry calculations. TAFFI formalizes the concept of atom typing and makes it the basis for generating systematic training data that maintains a one-to-one correspondence with force-field terms. This feature makes TAFFI arbitrarily extensible to new chemistries while maintaining internal consistency and transferability. As a demonstration of TAFFI, we have developed a fixed-charge force-field, TAFFI-gen, from scratch that includes coverage for common organic functional groups that is comparable to established transferable force fields. The performance of TAFFI-gen was benchmarked against OPLS and GAFF for reproducing several experimental properties of 87 organic liquids. The consistent performance of these force fields, despite their distinct origins, validates the TAFFI framework while also providing evidence of the representability limitations of fixed-charge force fields.

Home Coping Strategies for Fatigue Used by Patients With Lung Cancer Receiving Chemotherapy in Rural China: A Qualitative Study.

BACKGROUND: Cancer-related fatigue, a distressing symptom, is frequently reported by patients with lung cancer as increasing in severity with the number of rounds of chemotherapy. Yet, patients and healthcare providers are challenged to control this fatigue. Thus, healthcare providers must have interventions to effectively enhance coping engagement in patients with lung cancer. PURPOSE: The aims of this study were to explore how patients with lung cancer in a rural area of China undergoing chemotherapy cope with the fatigue at home and to summarize their strategies. METHODS: A descriptive qualitative research approach was used, and data were collected using semistructured interviews. Sixteen patients with lung cancer with chemotherapy-related fatigue living in rural communities were recruited from a large, tertiary teaching hospital in Huzhou in eastern China. The transcripts of the interviews were analyzed using content analysis. RESULTS: Coping strategies for cancer-related fatigue were delineated into the three themes of (a) psychological adjustment, (b) efforts to change lifestyles and act as a Chinese health practitioner, and (c) relying on social support. CONCLUSIONS/IMPLICATIONS FOR PRACTICE: The participants in this study provided information on a variety of approaches to reducing/alleviating cancer-related fatigue that were influenced by Chinese culture. Healthcare providers and patients may work together in clinical settings to identify appropriate, effective coping solutions and then to incorporate these into the regular care regimen to help patients transition between hospital and home.

Transferable Ring Corrections for Predicting Enthalpy of Formation of Cyclic Compounds.

Computational predictions of the thermodynamic properties of molecules and materials play a central role in contemporary reaction prediction and kinetic modeling. Due to the lack of experimental data and computational cost of high-level quantum chemistry methods, approximate methods based on additivity schemes and more recently machine learning are currently the only approaches capable of supplying the chemical coverage and throughput necessary for such applications. For both approaches, ring-containing molecules pose a challenge to transferability due to the nonlocal interactions associated with conjugation and strain that significantly impact thermodynamic properties. Here, we report the development of a self-consistent approach for parameterizing transferable ring corrections based on high-level quantum chemistry. The method is benchmarked against both the Pedley-Naylor-Kline experimental dataset for C-, H-, O-, N-, S-, and halogen-containing cyclic molecules and a dataset of Gaussian-4 quantum chemistry calculations. The prescribed approach is demonstrated to be superior to existing ring corrections while maintaining extensibility to arbitrary chemistries. We have also compared this ring-correction scheme against a novel machine learning approach and demonstrate that the latter is capable of exceeding the performance of physics-based ring corrections.

Risk factors for postoperative hypocalcaemia after thyroidectomy: A systematic review and meta-analysis.

OBJECTIVE: A meta-analysis to investigate the risk factors for postoperative hypocalcaemia after thyroidectomy in adult patients. METHODS: A systematic search of publications in the electronic databases (PubMed®, The Cochrane Library, Web of Science, OVID and Embase®) from inception to June 2020 was conducted. Screening of titles, abstracts and full texts and data extraction were independently performed by two authors. The OR was selected as the pooled estimate. RESULTS: The analysis included 23 studies. Twelve significant risk factors for postoperative hypocalcaemia were identified: hypoparathyroidism, OR 5.58; total thyroidectomy, OR 3.59; hypomagnesaemia, OR 2.85; preoperative vitamin D deficiency, OR 2.32; female sex, OR 1.49; thyroid malignancy, OR 1.85; thyroiditis, OR 1.48; substernal multinodular goitres, OR 1.70; parathyroidectomy, OR 1.58; central compartment neck dissection, OR 1.17; modified radical neck dissection, OR 1.57; and central neck dissection, OR 1.54. CONCLUSIONS: This meta-analysis provides moderate-to-high quality evidence that the 12 risk factors were predictive of postoperative hypocalcaemia, which should be monitored closely before thyroidectomy.

Efficacy and safety of aliskiren combination therapy: a protocol for an umbrella review.

INTRODUCTION: Efficacy of aliskiren combination therapy with other antihypertensive has been evaluated in the treatment of patients with hypertension in recent systematic reviews. However, most previous reviews only focused on one single health outcome or one setting, none of them made a full summary that assessed the impact of aliskiren combination treatment comprehensively. As such, this umbrella review based on systematic reviews and meta-analyses is aimed to synthesise the evidences on efficacy, safety and tolerability of aliskiren-based therapy for hypertension and related comorbid patients. METHODS AND ANALYSIS: A comprehensive search of PubMed, EMBASE, Cochrane Library, CNKI published from inception to August 2020 will be conducted. The selected articles are systematic reviews which evaluated efficacy, safety and tolerability of aliskiren combination therapy. Two reviewers will screen eligible articles, extract data and evaluate quality independently. Any disputes will be resolved by discussion or the arbitration of a third person. The quality of reporting evidence will be assessed using the Assessment of Multiple Systematic Reviews V.2 tool tool. We will take a mixed-methods approach to synthesising the review literatures, reporting summary of findings tables and iteratively mapping the results. ETHICS AND DISSEMINATION: Ethical approval is not required for the study, as we would only collect data from available published materials. This umbrella review will be also submitted to a peer-reviewed journal for publication after completion. PROSPERO REGISTRATION NUMBER: CRD42020192131.

Irrational beliefs surrounding the diagnosis of breast cancer in young Chinese women: An observational study.

ABSTRACT: An irrational belief is the direct cause of negative emotions and behavioral disorders in patients with breast cancer. Thus, this article examines these patients' irrational beliefs, which helps improve the emotions and behavioral disorders of breast cancer patients. Chinese breast cancer patients have unique irrational beliefs due to the influence of Chinese traditional culture. To understand the irrational beliefs surrounding breast cancer diagnosis in young Chinese patients, we conducted an interpretative phenomenological study.Semi-structured interviews were conducted in young Chinese breast cancer patients. According to Colaizzi method modified by Edward and Welsh, transcribed interviews were analyzed to understand patients' irrational beliefs. Based on the theoretical framework, this study adopted interpretative phenomenology. Interpretive description was used to construct participants' experiences of irrational beliefs. Thematic sufficiency was confirmed after 17 interviews.Owing to the lack of knowledge about breast cancer, all participants were more susceptible to traditional Chinese culture, empiric theory, family reassurance, and healthcare providers' behaviors, leading to patients' irrational beliefs, negative emotions, and behavioral disorders.This research confirms that irrational beliefs in young Chinese breast cancer patients are profoundly influenced by traditional Chinese culture. Chinese healthcare providers can use this information to provide targeted nursing, supportive services, and research, and help women identify their beliefs and understand how these beliefs affect their health.

Synthesis and evaluation of new compounds bearing 3-(4-aminopiperidin-1-yl)methyl magnolol scaffold as anticancer agents for the treatment of non-small cell lung cancer via targeting autophagy.

Magnolol and honokiol are the two major active ingredients with similar structure and anticancer activity from traditional Chinese medicine Magnolia officinalis, and honokiol is now in a phase I clinical trial (CTR20170822) for advanced non-small cell lung cancer (NSCLC). In search of potent lead compounds with better activity, our previous study has demonstrated that magnolol derivative C2, 3-(4-aminopiperidin-1-yl)methyl magnolol, has better activity than honokiol. Here, based on the core of 3-(4-aminopiperidin-1-yl)methyl magnolol, we synthesized fifty-one magnolol derivatives. Among them, compound 30 exhibited the most potent antiproliferative activities on H460, HCC827, H1975 cell lines with the IC50 values of 0.63-0.93 µM, which were approximately 10- and 100-fold more potent than those of C2 and magnolol, respectively. Besides, oral administration of 30 and C2 on an H460 xenograft model also demonstrated that 30 has better activity than C2. Mechanism study revealed that 30 induced G0/G1 phase cell cycle arrest, apoptosis and autophagy in cancer cells. Moreover, blocking autophagy by the autophagic inhibitor enhanced the anticancer activity of 30in vitro and in vivo, suggesting autophagy played a cytoprotective role on 30-induced cancer cell death. Taken together, our study implied that compound 30 combined with autophagic inhibitor could be another choice for NSCLC treatment in further investigation.

Simultaneously improving reaction coverage and computational cost in automated reaction prediction tasks.

Automated reaction prediction has the potential to elucidate complex reaction networks for applications ranging from combustion to materials degradation, but computational cost and inconsistent reaction coverage are still obstacles to exploring deep reaction networks. Here we show that cost can be reduced and reaction coverage can be increased simultaneously by relatively straightforward modifications of the reaction enumeration, geometry initialization and transition state convergence algorithms that are common to many prediction methodologies. These components are implemented in the context of yet another reaction program (YARP), our reaction prediction package with which we report reaction discovery benchmarks for organic single-step reactions, thermal degradation of a γ-ketohydroperoxide, and competing ring-closures in a large organic molecule. Compared with recent benchmarks, YARP (re)discovers both established and unreported reaction pathways and products while simultaneously reducing the cost of reaction characterization by nearly 100-fold and increasing convergence of transition states. This combination of ultra-low cost and high reaction coverage creates opportunities to explore the reactivity of larger systems and more complex reaction networks for applications such as chemical degradation, where computational cost is a bottleneck.