What Distinguishes the Strength and the Effect of a Lewis Acid: Analysis of the Gutmann–Beckett Method

IUPAC defines Lewis acidity as the thermodynamic tendency for Lewis pair formation. This strength property was recently specified as global Lewis acidity (gLA), and is gauged for example by the fluoride ion affinity. Experimentally, Lewis acidity is usually evaluated by the effect on a bound molecule, such as the induced 31P NMR shift of triethylphosphine oxide in the Gutmann–Beckett (GB) method. This type of scaling was called effective Lewis acidity (eLA). Unfortunately, gLA and eLA often correlate poorly, but a reason for this is unknown. Hence, the strength and the effect of a Lewis acid are two distinct properties, but they are often granted interchangeably. The present work analyzes thermodynamic, NMR specific, and London dispersion effects on GB numbers for 130 Lewis acids by theory and experiment. The deformation energy of a Lewis acid is identified as the prime cause for the critical deviation between gLA and eLA but its correction allows a unification for the first time

A likelihood-based reconstruction algorithm for top-quark pairs and the KLFitter framework

A likelihood-based reconstruction algorithm for arbitrary event topologies is introduced and, as an example, applied to the single-lepton decay mode of top-quark pair production. The algorithm comes with several options which further improve its performance, in particular the reconstruction efficiency, i.e., the fraction of events for which the observed jets and leptons can be correctly associated with the final-state particles of the corresponding event topology. The performance is compared to that of well-established reconstruction algorithms using a common framework for kinematic fitting. This framework has a modular structure which describes the physics processes and detector models independently. The implemented algorithms are generic and can easily be ported from one experiment to another.Comment: 20 pages, 5 figures, 2 table

A SITUATION AWARENESS DRIVEN DESIGN FOR PREDICTIVE MAINTENANCE SYSTEMS: THE CASE OF OIL AND GAS PIPELINE OPERATIONS

The acquisition and processing of events from sensors or enterprise applications in real-time represent an essential part of many application domains such as the Internet of Things (IoT), offering benefits to predict the future condition of equipment to prevent the occurrence of failures. Many organisations already use some form of predictive maintenance to monitor performance or keep track of emerging business situations. However, the optimal design of applications to allow an effective Predictive Mainte-nance System (PMS) capable of analysing and processing large amounts of data is only scarcely exam-ined by Information Systems (IS) research. Due to the number, frequency, and the need for near-real-time evaluation systems must be capable of detecting complex event patterns based on spatial, temporal, or causal relationships on data streams (i.e. via Complex Event Processing). At the same time, however, due to the technical complexity, available systems today are static, since the creation and adaptation of recognisable situations results in slow development cycles. In addition, technical feasibility is only one prerequisite for predictive maintenance. Users must be capable of processing this vast amount of data presented without considerable cognitive effort. Precisely this challenge is even more daunting as op-erational maintenance personnel have to manage business-critical decisions with increasing frequency and short time. Research in Human Factors (HF) suggests Situation Awareness (SA) as a crucial sys-tem’s design paradigm allowing human beings to understand and anticipate the information available effectively. Building on this concept, this paper proposes a PMS for promoting operational decision makers’ Situation Awareness by three design principles (DP): Sensing, Acting, and Tracking. Based on these DPs, we implemented a PMS prototype for a scenario in Oil and Gas pipeline operations. Our finding suggest that the use of SA is of particular interest in realizing effective PMS

Comparative Life Cycle Assessment Of Conventionally Manufactured And Additive Remanufactured Electric Bicycle Motors

In a circular economy, remanufacturing is crucial in reducing the use of primary raw materials and energy compared to new production. However, poor availability of non-standardized wear components can prevent remanufacturing. Additive manufacturing is a promising alternative to conventional manufacturing or spare part purchase for those wear components required for remanufacturing. However, there is uncertainty regarding the environmental impact of using additive manufacturing for remanufacturing. This paper compares conventional and additive spare parts manufacturing to evaluate the potential environmental savings of remanufacturing electric bicycle motors. Therefore, a reference motor was selected, and its manufacturing processes were modeled in SimaPro using the ecoinvent 3.8 Life Cycle Assessment database and the latest knowledge on processing and manufacturing processes. The results show that conventional production of electric bicycle motors has a climate warming potential of around 28 kg CO2-eq. Additive remanufacturing of electric bicycle motors at the end of their life cycle offers significant environmental savings potential. The extent of savings depends on the condition of the used electric bicycle motor and, accordingly, the number of components that need to be replaced. According to the IPCC method for the electric bicycle motor investigated, the study estimates that approximately 90.4 % savings potential can be achieved in terms of Global Warming Potential

Multiclass cancer classification in fresh frozen and formalin-fixed paraffin-embedded tissue by DigiWest multiplex protein analysis

Histomorphology and immunohistochemistry are the most common ways of cancer classification in routine cancer diagnostics, but often reach their limits in determining the organ origin in metastasis. These cancers of unknown primary, which are mostly adenocarcinomas or squamous cell carcinomas, therefore require more sophisticated methodologies of classification. Here, we report a multiplex protein profiling-based approach for the classification of fresh frozen and formalin-fixed paraffin-embedded (FFPE) cancer tissue samples using the digital western blot technique DigiWest. A DigiWest-compatible FFPE extraction protocol was developed, and a total of 634 antibodies were tested in an initial set of 16 FFPE samples covering tumors from different origins. Of the 303 detected antibodies, 102 yielded significant correlation of signals in 25 pairs of fresh frozen and FFPE primary tumor samples, including head and neck squamous cell carcinomas (HNSC), lung squamous cell carcinomas (LUSC), lung adenocarcinomas (LUAD), colorectal adenocarcinomas (COAD), and pancreatic adenocarcinomas (PAAD). For this signature of 102 analytes (covering 88 total proteins and 14 phosphoproteins), a support vector machine (SVM) algorithm was developed. This allowed for the classification of the tissue of origin for all five tumor types studied here with high overall accuracies in both fresh frozen (90.4%) and FFPE (77.6%) samples. In addition, the SVM classifier reached an overall accuracy of 88% in an independent validation cohort of 25 FFPE tumor samples. Our results indicate that DigiWest-based protein profiling represents a valuable method for cancer classification, yielding conclusive and decisive data not only from fresh frozen specimens but also FFPE samples, thus making this approach attractive for routine clinical applications

Intermediate field-induced phase of the honeycomb magnet BaCo2_2(AsO4_4)2_2

We use magnetometry, calorimetry, and high-resolution capacitive dilatometry, as well as single-crystal neutron diffraction to explore temperature-field phase diagram of the anisotropic honeycomb magnet BaCo$_2$(AsO$_4)_2$. Our data reveal four distinct ordered states observed for in-plane magnetic fields. Of particular interest is the narrow region between 0.51 and 0.55 T that separates the up-up-down order from the fully polarized state and coincides with the field range where signatures of the spin-liquid behavior have been reported. We show that magnetic Bragg peaks persist in this intermediate phase, thus ruling out its spin-liquid nature. However, the simultaneous nonmonotonic evolution of nuclear Bragg peaks suggests the involvement of the lattice, witnessed also in other regions of the phase diagram where large changes in the sample length are observed upon entering the magnetically ordered states. Our data highlight the importance of lattice effects in BaCo$_2$(AsO$_4)_2$

Edgetic Perturbations Contribute to Phenotypic Variability in PEX26 Deficiency

Peroxisomes share metabolic pathways with other organelles and peroxisomes are embedded into key cellular processes. However, the specific function of many peroxisomal proteins remains unclear and restricted knowledge of the peroxisomal protein interaction network limits a precise mapping of this network into the cellular metabolism. Inborn peroxisomal disorders are autosomal or X-linked recessive diseases that affect peroxisomal biogenesis (PBD) and/or peroxisomal metabolism. Pathogenic variants in the PEX26 gene lead to peroxisomal disorders of the full Zellweger spectrum continuum. To investigate the phenotypic complexity of PEX26 deficiency, we performed a combined organelle protein interaction screen and network medicine approach and 1) analyzed whether PEX26 establishes interactions with other peroxisomal proteins, 2) deciphered the PEX26 interaction network, 3) determined how PEX26 is involved in further processes of peroxisomal biogenesis and metabolism, and 4) showed how variant-specific disruption of protein-protein interactions (edgetic perturbations) may contribute to phenotypic variability in PEX26 deficient patients. The discovery of 14 novel protein-protein interactions for PEX26 revealed a hub position of PEX26 inside the peroxisomal interactome. Analysis of edgetic perturbations of PEX26 variants revealed a strong correlation between the number of affected protein-protein interactions and the molecular phenotype of matrix protein import. The role of PEX26 in peroxisomal biogenesis was expanded encompassing matrix protein import, division and proliferation, and membrane assembly. Moreover, the PEX26 interaction network intersects with cellular lipid metabolism at different steps. The results of this study expand the knowledge about the function of PEX26 and refine genotype-phenotype correlations, which may contribute to our understanding of the underlying disease mechanism of PEX26 deficiency