‘Frustrated’ Lewis pairs: From Lewis acid -base adducts to the reversible, metal-free activation of hydrogen

The concept of \u27frustrated\u27 Lewis pairs involves donor and acceptor sites in which steric congestion precludes Lewis acid–base adduct formation. In the case of sterically demanding phosphines and some boranes, this lack of active site-quenching prompts nucleophilic attack by P at a carbon para to B of B(C6F5)3 followed by fluoride transfer, which affords zwitterionic phosphonium borates of the form [R3P(C6F4)BF(C6F5) 2] and [R2PH(C6F4)BF(C6F 5)2], where R = aryl, alkyl. Additionally, a series of tertiary and secondary phosphine-B(C6F5)3 adducts are shown to undergo facile, thermal-induced rearrangement to give analogous zwitterionic species of the form [R3P(C6F4)BF(C 6F5)2] and [R2PH(C6F 4)BF(C6F5)2], respectively, where R = aryl, alkyl. These species can be easily transformed into anionic phosphine-borates [R2P(C6F4)BF(C6F5) 2]-, cationic phosphonium-boranes [R3P(C 6F4)B(C6F5)2]+ and [R2PH(C6F4)B(C6F5) 2]+ or the charge neutral phosphino-boranes [R2 P(C6F4)BF(C6F5)2]. This new reactivity provides a modular route to a family of boranes in which the steric features about the Lewis acidic boron center remain constant and yet the variation in substitution at phosphorus provides a facile avenue for the tuning of the Lewis acidity. Employing the Gutmann–Beckett and Childs methods for determining Lewis acid strength, it was demonstrated that the cationic boranes are more Lewis acidic than B(C6F5) 3, while the acidity of the phosphino-boranes is diminished. Sterically demanding tertiary and secondary phosphines, as well as secondary phosphides, have been shown to react with (THF)B(C6F5) 3 (THF = tetrahydrofuran) to give the THF ring-opened compounds [R 3P(C6H8O)B(C6F5)3], [R2PH(C4H8O)B(C6F5) 3] and [R2P(C4H8O)B(C6F 5)3Li(THF)2] (R = aryl, alkyl). With appropriate stoichiometry, these reactions also occur consecutively to give the double THE ring-opened compounds [Mes2P(C4H8OB(C 6F5)3)2] [Li(THF)4] and rBu2P(C4H8OB(C6F5)3) 2][Li]. Finally, it has been reported that the compounds [R2P(C 6F4)BH(C6F5)2]2 (R = aryl or alkyl), cleanly liberate H2 at temperatures above 100 °C to give the dehydrogenated products R2P(C6F4)B(C 6F5)2, which are stable and react with 1 atmosphere of H2 at 25°C to reform the starting complex. Combinations of sterically demanding phosphines R3P and B(C6F 5)3 also uptake H2 at ambient temperature and pressure. H2 liberation from the series of compounds can be facilitated using a weak Lewis base. Preliminary kinetic and deuterium labelling experiments indicate that the reversible activation of H2 follows an intermolecular mechanism

Application of accounting principles to a course in accounting for small contractors.

Thesis (M.A.)--Boston University This item was digitized by the Internet Archive

Fertilization and Liming to Establish and Maintain Vegetative Cover for Soil Conservation.

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Zinc Deficiency and Fertilization.

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A Metabolic Dependency for Host Isoprenoids in the Obligate Intracellular Pathogen Rickettsia parkeri Underlies a Sensitivity to the Statin Class of Host-Targeted Therapeutics.

Gram-negative bacteria in the order Rickettsiales have an obligate intracellular growth requirement, and some species cause human diseases such as typhus and spotted fever. The bacteria have evolved a dependence on essential nutrients and metabolites from the host cell as a consequence of extensive genome reduction. However, it remains largely unknown which nutrients they acquire and whether their metabolic dependency can be exploited therapeutically. Here, we describe a genetic rewiring of bacterial isoprenoid biosynthetic pathways in the Rickettsiales that has resulted from reductive genome evolution. Furthermore, we investigated whether the spotted fever group Rickettsia species Rickettsia parkeri scavenges isoprenoid precursors directly from the host. Using targeted mass spectrometry, we found that infection caused decreases in host isoprenoid products and concomitant increases in bacterial isoprenoid metabolites. Additionally, we report that treatment of infected cells with statins, which inhibit host isoprenoid synthesis, prohibited bacterial growth. We show that growth inhibition correlates with changes in bacterial size and shape that mimic those caused by antibiotics that inhibit peptidoglycan biosynthesis, suggesting that statins lead to an inhibition of cell wall synthesis. Altogether, our results describe a potential Achilles' heel of obligate intracellular pathogens that can potentially be exploited with host-targeted therapeutics that interfere with metabolic pathways required for bacterial growth.IMPORTANCE Obligate intracellular pathogens, which include viruses as well as certain bacteria and eukaryotes, are a subset of infectious microbes that are metabolically dependent on and unable to grow outside an infected host cell because they have lost or lack essential biosynthetic pathways. In this study, we describe a metabolic dependency of the bacterial pathogen Rickettsia parkeri on host isoprenoid molecules that are used in the biosynthesis of downstream products, including cholesterol, steroid hormones, and heme. Bacteria make products from isoprenoids, such as an essential lipid carrier for making the bacterial cell wall. We show that bacterial metabolic dependency can represent a potential Achilles' heel and that inhibiting host isoprenoid biosynthesis with the FDA-approved statin class of drugs inhibits bacterial growth by interfering with the integrity of the cell wall. This work supports the potential to treat infections by obligate intracellular pathogens through inhibition of host biosynthetic pathways that are susceptible to parasitism

Leveraging Longitudinal Data for Personalized Prediction and Word Representations

This thesis focuses on personalization, word representations, and longitudinal dialog. We first look at users expressions of individual preferences. In this targeted sentiment task, we find that we can improve entity extraction and sentiment classification using domain lexicons and linear term weighting. This task is important to personalization and dialog systems, as targets need to be identified in conversation and personal preferences affect how the system should react. Then we examine individuals with large amounts of personal conversational data in order to better predict what people will say. We consider extra-linguistic features that can be used to predict behavior and to predict the relationship between interlocutors. We show that these features improve over just using message content and that training on personal data leads to much better performance than training on a sample from all other users. We look not just at using personal data for these end-tasks, but also constructing personalized word representations. When we have a lot of data for an individual, we create personalized word embeddings that improve performance on language modeling and authorship attribution. When we have limited data, but we have user demographics, we can instead construct demographic word embeddings. We show that these representations improve language modeling and word association performance. When we do not have demographic information, we show that using a small amount of data from an individual, we can calculate similarity to existing users and interpolate or leverage data from these users to improve language modeling performance. Using these types of personalized word representations, we are able to provide insight into what words vary more across users and demographics. The kind of personalized representations that we introduce in this work allow for applications such as predictive typing, style transfer, and dialog systems. Importantly, they also have the potential to enable more equitable language models, with improved performance for those demographic groups that have little representation in the data.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167971/1/cfwelch_1.pd

Unifying Data Perspectivism and Personalization: An Application to Social Norms

Instead of using a single ground truth for language processing tasks, several recent studies have examined how to represent and predict the labels of the set of annotators. However, often little or no information about annotators is known, or the set of annotators is small. In this work, we examine a corpus of social media posts about conflict from a set of 13k annotators and 210k judgements of social norms. We provide a novel experimental setup that applies personalization methods to the modeling of annotators and compare their effectiveness for predicting the perception of social norms. We further provide an analysis of performance across subsets of social situations that vary by the closeness of the relationship between parties in conflict, and assess where personalization helps the most

Challenges of GPT-3-based Conversational Agents for Healthcare

The potential to provide patients with faster information access while allowing medical specialists to concentrate on critical tasks makes medical domain dialog agents appealing. However, the integration of large-language models (LLMs) into these agents presents certain limitations that may result in serious consequences. This paper investigates the challenges and risks of using GPT-3-based models for medical question-answering (MedQA). We perform several evaluations contextualized in terms of standard medical principles. We provide a procedure for manually designing patient queries to stress-test high-risk limitations of LLMs in MedQA systems. Our analysis reveals that LLMs fail to respond adequately to these queries, generating erroneous medical information, unsafe recommendations, and content that may be considered offensive.Comment: 12 pages, 9 Tables, accepted to RANLP 202