Towards automation of chemical process route selection based on data mining

A methodology for chemical routes development and evaluation on the basis of data-mining is presented. A section of the Reaxys database was converted into a network, which was used to plan hypothetical synthesis routes to convert a bio-waste feedstock, limonene, to a bulk intermediate, benzoic acid. The route evaluation considered process conditions and used multiple indicators, including exergy, E-factor, solvent score, reaction reliability and route redox efficiency, in a multi-criteria environmental sustainability evaluation. The proposed methodology is the first route evaluation based on data mining, explicitly using reaction conditions, and is amenable to full automation.This work was in part funded by EPSRC project “Terpene-based manufacturing for sustainable chemical feedstocks” EP/K014889

A possible extension to the RInChI as a means of providing machine readable process data

The algorithmic, large-scale use and analysis of reaction databases such as Reaxys is currently hindered by the absence of widely adopted standards for publishing reaction data in machine readable formats. Crucial data such as yields of all products or stoichiometry are frequently not explicitly stated in the published papers and, hence, not reported in the database entry for those reactions, limiting their usefulness for algorithmic analysis. This paper presents a possible extension to the IUPAC RInChI standard via an auxiliary layer, termed $\textit{ProcAuxInfo}$, which is a standardised, extensible form in which to report certain key reaction parameters such as declaration of all products and reactants as well as auxiliaries known in the reaction, reaction stoichiometry, amounts of substances used, conversion, yield and operating conditions. The standard is demonstrated via creation of the RInChI including the $\textit{ProcAuxInfo}$ layer based on three published reactions and demonstrates accurate data recoverability via reverse translation of the created strings. Implementation of this or another method of reporting process data by the publishing community would ensure that databases, such as Reaxys, would be able to abstract crucial data for big data analysis of their contents.PMJ is grateful to Peterhouse and the Cambridge Trust for scholarships

Conjugated Polyimidazole Nanoparticles as Biodegradable Electrode Materials for Organic Batteries

Conjugated polymers are promising active materials for batteries. Batteries not only need to have high energy density but should also combine safe handling with recyclability or biodegradability after reaching their end-of-life. Here, π-conjugated polyimidazole particles are developed, which are prepared using atom economic direct arylation adapted to a dispersion polymerization protocol. The synthesis yields polyimidazole nanoparticles of tunable size and narrow dispersity. In addition, the degree of crosslinking of the polymer particles can be controlled. It is demonstrated that the polyimidazole nanoparticles can be processed together with carbon black and biodegradable carboxymethyl cellulose binder as an active material for organic battery electrodes. Electrochemical characterization shows that a higher degree of crosslinking significantly improves the electrochemical performance and leads to clearer oxidation and reduction signals of the polymer. Polyimidazole as part of the composite electrode shows complete degradation by exposure to composting bacteria over the course of 72 h

Enabling Mobility-Oriented JCAS in 6G Networks: An Architecture Proposal

Sensing plays a crucial role in autonomous and assisted vehicular driving, as well as in the operation of autonomous drones. The traditional segregation of communication and onboard sensing systems in mobility applications is due to be merged using Joint Communication and Sensing (JCAS) in the development of the 6G mobile radio standard. The integration of JCAS functions into the future road traffic landscape introduces novel challenges for the design of the 6G system architecture. Special emphasis will be placed on facilitating direct communication between road users and aerial drones. In various mobility scenarios, diverse levels of integration will be explored, ranging from leveraging communication capabilities to coordinate different radars to achieving deep integration through a unified waveform. In this paper, we have identified use cases and derive five higher-level Tech Cases (TCs). Technical and functional requirements for the 6G system architecture for a device-oriented JCAS approach will be extracted from the TCs and used to conceptualize the architectural views.Comment: 6 pages, 3 figures, 4th IEEE Symposium on Joint Communication and Sensin

Molecular Predictors of Immunophenotypic Measurable Residual Disease Clearance in Acute Myeloid Leukemia

Measurable residual disease (MRD) is a powerful prognostic factor in acute myeloid leukemia (AML). However, pre-treatment molecular predictors of immunophenotypic MRD clearance remain unclear. We analyzed a dataset of 211 patients with pre-treatment next-generation sequencing who received induction chemotherapy and had MRD assessed by serial immunophenotypic monitoring after induction, subsequent therapy, and allogeneic stem cell transplant (allo-SCT). Induction chemotherapy led to MRD- remission, MRD+ remission, and persistent disease in 35%, 27%, and 38% of patients, respectively. With subsequent therapy, 34% of patients with MRD+ and 26% of patients with persistent disease converted to MRD-. Mutations in CEBPA, NRAS, KRAS, and NPM1 predicted high rates of MRD- remission, while mutations in TP53, SF3B1, ASXL1, and RUNX1 and karyotypic abnormalities including inv (3), monosomy 5 or 7 predicted low rates of MRD- remission. Patients with fewer individual clones were more likely to achieve MRD- remission. Among 132 patients who underwent allo-SCT, outcomes were favorable whether patients achieved early MRD- after induction or later MRD- after subsequent therapy prior to allo-SCT. As MRD conversion with chemotherapy prior to allo-SCT is rarely achieved in patients with specific baseline mutational patterns and high clone numbers, upfront inclusion of these patients into clinical trials should be considered

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

In COVID-19 Health Messaging, Loss Framing Increases Anxiety with Little-to-No Concomitant Benefits: Experimental Evidence from 84 Countries

The COVID-19 pandemic (and its aftermath) highlights a critical need to communicate health information effectively to the global public. Given that subtle differences in information framing can have meaningful effects on behavior, behavioral science research highlights a pressing question: Is it more effective to frame COVID-19 health messages in terms of potential losses (e.g., "If you do not practice these steps, you can endanger yourself and others") or potential gains (e.g., "If you practice these steps, you can protect yourself and others")? Collecting data in 48 languages from 15,929 participants in 84 countries, we experimentally tested the effects of message framing on COVID-19-related judgments, intentions, and feelings. Loss- (vs. gain-) framed messages increased self-reported anxiety among participants cross-nationally with little-to-no impact on policy attitudes, behavioral intentions, or information seeking relevant to pandemic risks. These results were consistent across 84 countries, three variations of the message framing wording, and 560 data processing and analytic choices. Thus, results provide an empirical answer to a global communication question and highlight the emotional toll of loss-framed messages. Critically, this work demonstrates the importance of considering unintended affective consequences when evaluating nudge-style interventions

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis