Student Impressions of Syllabus Design: Engaging Versus Contractual Syllabus

This study compared student impressions of a text-rich contractual syllabus to a graphic-rich engaging syllabus. Students enrolled in sections of an undergraduate introductory nutrition course viewed either a contractual or engaging syllabus and completed a survey regarding their perceptions of the course and instructor. Students perceived both types of syllabus positively, yet the engaging syllabus was judged to be more visually appealing and comprehensive. More importantly, it motivated more interest in the class and instructor than the contractual syllabus. Using an engaging syllabus may benefit instructors who seek to gain more favorable initial course perceptions by students. This study compared student impressions of a text-rich contractual syllabus to a graphic-rich engaging syllabus. Students enrolled in sections of an undergraduate introductory nutrition course viewed either a contractual or engaging syllabus and completed a survey regarding their perceptions of the course and instructor. Students perceived both types of syllabus positively, yet the engaging syllabus was judged to be more visually appealing and comprehensive. More importantly, it motivated more interest in the class and instructor than the contractual syllabus. Using an engaging syllabus may benefit instructors who seek to gain more favorable initial course perceptions by students

Evaluation of a Bayesian inference network for ligand-based virtual screening

Background Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query. Here, we modify the approach to enable a Bayesian inference network to be used for chemical similarity searching, where a database is ranked in order of decreasing probability of bioactivity. Results Bayesian inference networks were implemented using two different types of network and four different types of belief function. Experiments with the MDDR and WOMBAT databases show that a Bayesian inference network can be used to provide effective ligand-based screening, especially when the active molecules being sought have a high degree of structural homogeneity; in such cases, the network substantially out-performs a conventional, Tanimoto-based similarity searching system. However, the effectiveness of the network is much less when structurally heterogeneous sets of actives are being sought. Conclusion A Bayesian inference network provides an interesting alternative to existing tools for ligand-based virtual screening

Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed

A synergistic ozone-climate control to address emerging ozone pollution challenges

Tropospheric ozone threatens human health and crop yields, exacerbates global warming, and fundamentally changes atmospheric chemistry. Evidence has pointed toward widespread ozone increases in the troposphere, and particularly surface ozone is chemically complex and difficult to abate. Despite past successes in some regions, a solution to new challenges of ozone pollution in a warming climate remains unexplored. In this perspective, by compiling surface measurements at ∼4,300 sites worldwide between 2014 and 2019, we show the emerging global challenge of ozone pollution, featuring the unintentional rise in ozone due to the uncoordinated emissions reduction and increasing climate penalty. On the basis of shared emission sources, interactive chemical mechanisms, and synergistic health effects between ozone pollution and climate warming, we propose a synergistic ozone-climate control strategy incorporating joint control of ozone and fine particulate matter. This new solution presents an opportunity to alleviate tropospheric ozone pollution in the forthcoming low-carbon transition.This study was supported by the Research Grants Council of Hong Kong Special Administrative Region via General Research Funds (HKBU 15219621 and PolyU 15212421) and a Theme-based Research Scheme (T24-504/17-N). The authors acknowledge the support of the Australia–China Centre on Air Quality Science and Management. R.S. acknowledges support from ANID/FONDAP/1522A0001. D.S. thanks the program of Coordination for the Improvement of Higher Education Personnel (CAPES) (436466/2018-0). X.X. acknowledges funding from the Natural Science Foundation of China (41330422) and the Chinese Academy of Meteorological Sciences (2020KJ003). K.L. is supported by the Natural Science Foundation of China (42205114), Jiangsu Carbon Peak and Neutrality Science and Technology Innovation fund (BK20220031), and the Startup Foundation for Introducing Talent of NUIST. We sincerely appreciate all the organizations and programs introduced in the section “experimental procedures” for freely providing ozone data. We thank Dr. Owen Cooper (University of Colorado, Boulder, and NOAA) for insightful guidance and discussion. No organization or program will be responsible for the results generated from their data.Peer reviewe

Elife

Compartmental models are the theoretical tool of choice for understanding single neuron computations. However, many models are incomplete, built ad hoc and require tuning for each novel condition rendering them of limited usability. Here, we present T2N, a powerful interface to control NEURON with Matlab and TREES toolbox, which supports generating models stable over a broad range of reconstructed and synthetic morphologies. We illustrate this for a novel, highly-detailed active model of dentate granule cells (GCs) replicating a wide palette of experiments from various labs. By implementing known differences in ion channel composition and morphology, our model reproduces data from mouse or rat, mature or adult-born GCs as well as pharmacological interventions and epileptic conditions. This work sets a new benchmark for detailed compartmental modeling. T2N is suitable for creating robust models useful for large-scale networks that could lead to novel predictions. We discuss possible T2N application in degeneracy studies

THERMAL MODEL FOR THE DESORPTION OF (MOLECULAR) IONS INDUCED BY MeV HEAVY IONS

Un modèle thermique a été développé pour décrire la désorption d'ions secondaires par des ions lourds de plusieurs MeV. Le modèle est capable de reproduire la dépendance du rendement d'émission avec l'énergie et la charge initiale des ions de plusieurs MeV.A thermal model is developed to describe the desorption of ions by MeV heavy ions. The model is able to reproduce the dependence of measured secondary ion yields on the energy and the initial charge state of the MeV ions