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Abstract Background Nucleic acid aptamers have long demonstrated the capacity to bind viral envelope proteins and to inhibit the progression of pathogenic virus infections. Here we report on initial efforts to develop and screen DNA aptamers against recombinant envelope proteins or synthetic peptides and whole inactivated viruses from several virulent arboviruses including Chikungunya, Crimean-Congo hemorrhagic fever (CCHF), dengue, tickborne encephalitis and West Nile viruses. We also analyzed sequence data and secondary structures for commonalities that might reveal consensus binding sites among the various aptamers. Some of the highest affinity and most specific aptamers in the down-selected libraries were demonstrated to have diagnostic utility in lateral flow chromatographic assays and in a fluorescent aptamer-magnetic bead sandwich assay. Some of the reported aptamers may also be able to bind viral envelope proteins in vivo and therefore may have antiviral potential in passive immunity or prophylactic applications. Results Several arbovirus DNA aptamer sequences emerged multiple times in the various down selected aptamer libraries thereby suggesting some consensus sequences for binding arbovirus envelope proteins. Screening of aptamers by enzyme-linked aptamer sorbent assay (ELASA) was useful for ranking relative aptamer affinities against their cognate viral targets. Additional study of the aptamer sequences and secondary structures of top-ranked anti-arboviral aptamers suggest potential virus binding motifs exist within some of the key aptamers and are highlighted in the supplemental figures for this article. One sequence segment (ACGGGTCCGGACA) emerged 60 times in the anti-CCHF aptamer library, but nowhere else in the anti-arbovirus library and only a few other times in a larger library of aptamers known to bind bacteria and rickettsia or other targets. Diagnostic utility of some of the aptamers for arbovirus detection in lateral flow chromatographic assays and a fluorescent sandwich assay on the surface of magnetic microbeads is also demonstrated. Conclusions This article catalogues numerous DNA aptamer sequences which can bind various important pathogenic arboviruses and have, in some cases, already demonstrated diagnostic potential. These aptamer sequences are proprietary, patent-pending, and partially characterized. Therefore, they are offered to the scientific community for potential research use in diagnostic assays, biosensor applications or for possible passive immunity and prophylaxis against pathogenic viruses.

In 2017, 43.9% of US physicians reported symptoms of burnout. Poor electronic health record (EHR) usability and time-consuming data entry contribute to burnout. However, less is known about how modifiable dimensions of EHR use relate to burnout and how these associations vary by medical specialty. Using the KLAS Arch Collaborative's large-scale nationwide physician (MD/DO) data, we used ordinal logistic regression to analyze associations between self-reported burnout and after-hours charting and organizational EHR support. We examined how these relationships differ by medical specialty, adjusting for confounders. Physicians reporting ≤ 5 hours weekly of after-hours charting were twice as likely to report lower burnout scores compared to those charting ≥6 hours (aOR: 2.43, 95% CI: 2.30, 2.57). Physicians who agree that their organization has done a great job with EHR implementation, training, and support (aOR: 2.14, 95% CI: 2.01, 2.28) were also twice as likely to report lower scores on the burnout survey question compared to those who disagree. Efforts to reduce after-hours charting and improve organizational EHR support could help address physician burnout.

This article evaluates the effect of discovery-based learning (DBL) in an undergraduate course in literature. The DBL method asked students in an introductory course on the Bible to discover facts about authorship and genres of various books through guided reading, followed by expository comments. The control method, in contrast, directly presented students with the expository comments. We show that students retained the target facts better in the short term with the DBL method than with the control method. Statistical analysis suggests that DBL plays a more decisive role in determining this outcome than the student's general academic competence or background. The gain with DBL is more significant for students who successfully discovered the target facts during treatment. According to a survey, twice as many students preferred DBL than the expository approach.

Extracurricular reading is important for learning foreign languages. Text recommendation systems typically classify users and documents into levels, and then match users with documents at the same level. Although this approach can be effective, it has two significant shortcomings. First, the levels assume a standard order of language acquisition and cannot be personalized to the users' learning patterns. Second, recommendation decisions are not transparent because the leveling algorithms can be difficult for users to interpret. We propose a novel method for text recommendation that addresses these two issues. To enhance personalization, an open, editable learner model estimates user knowledge of each word in the foreign language. The documents are ranked by new-word density (NWD) -- that is, the percentage of words that are new to the user in the document. The system then recommends documents according to a user-specified target NWD. This design offers complete transparency as users can scrutinize recommendations by reviewing the NWD estimation of the learner model. This article describes an implementation of this method in a mobile app for learners of Chinese as a foreign language. Evaluation results show that users were able to manipulate the learner model and NWD parameters to adjust the difficulty of the recommended documents. In a survey, users reported satisfaction with both the concept and implementation of this text recommendation method.

Unimolecular decomposition pathways are challenging to address in transition-metal catalysis and have previously not been suppressed via incorporation into a solid support. Two robust metal-organic frameworks (IRMOF-10 and bio-MOF-100) are used for the architectural stabilization of a structurally well-defined gold(III) catalyst. The inherent rigidity of these materials is utilized to preclude a unimolecular decomposition pathway - reductive elimination. Through this architectural stabilization strategy, decomposition of the incorporated gold(III) catalyst in the metal-organic frameworks is not observed; in contrast, the homogeneous analogue is prone to decomposition in solution. Stabilization of the catalyst in these metal-organic frameworks precludes leaching and enables recyclability, which is crucial for productive heterogeneous catalysis.

The synthesis is demonstrated for a heteroleptic Ni(II) complex, with a tridentate ligand bearing N-heterocyclic carbene (NHC), aryloxide, and picolyl donor groups. The compound was reacted with a variety of hydroxide sources, several of which resulted in complex mixtures. Reaction with [NH4]OH affords a bis-ligated nickel compound, in which the ligands are each bound in a bidentate fashion through the NHC and aryloxide donor groups. A desired nickel(II)-hydroxo product was not observed. DFT studies suggest that ligand redistribution is thermodynamically preferred over stabilization of a Ni-hydroxo in either a monomeric or dimeric form. [ABSTRACT FROM AUTHOR]

A text recommendation system helps language learners find suitable reading materials. Similar to graded readers, most systems assign difficulty levels or school grades to the documents in their database, and then identify the documents that best match the language proficiency of the learner. This graded approach has two main limitations. First, the common grade scale assumes that all learners find the same words difficult, ignoring different orders in vocabulary acquisition. Second, it cannot cater to the incremental vocabulary expansion typical of language learners, since discrete grades do not facilitate fine-grained adaptation in text recommendation. This article evaluates a personalized and adaptive method for text recommendation that aims to mitigate these limitations. This method recommends documents according to the new-word density preferred by the user. It estimates a document's new-word density with an open learner model that predicts the user's vocabulary knowledge. Users can dynamically update the learner model as they acquire new vocabulary. We conducted a user study involving ten learners of Chinese as a foreign language. Results shows that this method can estimate the learner model at 77.6% accuracy, outperforming the graded approach. Further, a user simulation demonstrates that it can recommend documents within 0.22% of the target new-word density, satisfying user preferences more accurately than the graded approach. [ABSTRACT FROM AUTHOR]