Expansive Framing as Pragmatic Theory for Online and Hybrid Instructional Design

This article explores the complex question of how instruction should be framed (i.e., contextualized). Reports from the US National Research Council reveal a broad consensus among experts that most instruction should be framed with problems, examples, cases, and illustrations. Such framing is assumed to help learners connect new knowledge to broader “real world” knowledge, motivate continued engagement, and ensure that learners can transfer their new knowledge to subsequent contexts. However, different theories of learning lead to different assumptions about when such frames should be introduced and how such frames should be created. This article shows how contemporary situative theories of learning argue that frames should be (a) introduced before instructional content, (b) generated by learners themselves, (c) used to make connections with people, places, topics, and times beyond the boundaries of the course, and (d) used to position learners as authors who hold themselves and their peers accountable for their participation in disciplinary discourse. This expansive approach to framing promises to support engagement with disciplinary content that is productive (i.e., increasingly sophisticated, raising new questions, recognizing confusion, making new connections, etc.) and generative (i.e., supporting transferable learning that is likely to be useful and used in a wide range of subsequent educational, professional, achievement, and personal contexts). A framework called Participatory Learning and Assessment (PLA) is presented that embeds expansively framed engagement within multiple levels of increasing formal assessments. This paper first summarizes PLA as theory-laden design principles. It then presents PLA as fourteen more prescriptive steps that some may find easier to implement and to learn as they go. Examples are presented from several courses from an extended program of design-based research using this approach in online and hybrid secondary, undergraduate, graduate, and technical courses.Indiana University Office of the Vice Provost of Information Technolog

Vers des assemblages de complexes métalliques oligonucléaires, servant d’antenne solaire au niveau moléculaire

Les fichiers additionnels sont les données cristallographiques en format CIF. Voir le site de la Cambridge Crystallographic Data Centre pour un visualiseur: http://www.ccdc.cam.ac.ukCe projet de recherche vise l’élaboration de systèmes métallosupramoléculaires artificiels imitant le processus naturel de la photosynthèse. Idéalement, ces systèmes seraient capables de fournir l’énergie et la séparation de charge nécessaire pour catalyser des réactions à transfert multiélectroniques, tel que l’hydrolyse de l’eau ou la réduction du gaz carbonique. La réalisation d’un tel système catalytique créerait une source d’énergie renouvelable, sous forme d’énergie chimique, crée directement à partir de l’énergie solaire. Le système envisagé, schématisé sous la forme d’une antenne, possède trois parties distinctes. Tout d’abord, des chromophores forment un état excité en captant l’énergie de la lumière visible du soleil. Vient ensuite un centre de liaison qui lie tous les chromophores et qui collecte l’énergie de cet état excité à travers un transfert d’électron. Cet électron est de nouveau transféré vers la dernière partie, un centre réactionnel catalytique. Cet assemblage permet de créer une séparation de charge entre le chromophore et le centre réactionnel qui sont séparés par le centre de liaison, évitant ainsi la recombinaison de charge. Le projet se focalise sur la synthèse, la caractérisation et l’application en photocatalyse d’assemblages chromophore–centre de liaison–catalyseur. Tout d’abord, une étude de chromophores à base de fluorène et de rhénium a été effectuée dans le but d’évaluer le transfert électronique entre ces deux composants. Ensuite, des centres de liaisons à base de dimère de rhodium tétraamidinate ont été créés et étudiés afin d’établir leurs caractéristiques photophysiques et électrochimiques. Puis un d’entre eux a été assemblé avec des chromophores de rhénium, créant ainsi des espèces moléculaires discrètes contenant d’un à quatre chromophores. Et pour finir, ces assemblages ont été combinés avec un catalyseur à base de cobalt, puis ont été testés dans des expériences de photoproduction d’hydrogène. Cette dernière partie a requis l’élaboration d’un photoréacteur qui est aussi décrite en détail dans cet ouvrage.This research project involves synthetic metallosupramolecular systems developed to mimic the natural process of photosynthesis. Ideally, these systems would be able to provide the energy and the charge separation needed to catalyze multielectron-transfer reactions, such as water-splitting or carbon dioxide reduction. The realization of such a catalytic system would create a renewable energy source, in the form of chemical energy, created directly from solar energy. The system envisioned has three distinct parts in the form of an antenna. First of all, chromophores go into an excited state, while capturing the visible light energy of the Sun. Then comes a hub which binds all the chromophores and collects this excited state energy through an electron transfer. This electron is then transferred again to the last part, a catalytic reaction center. This assembly creates a charge separation between the chromophore and the reaction center which are separated by the hub, thus avoiding the recombination of charge. The project focuses on the synthesis, characterization and application in photocatalysis of chromophore-hub-catalyst assemblies. First of all, a study of fluorene and rhenium based chromophores was made to assess the electronic transfer between these two components. Then, tetraamidinate rhodium dimer based hubs have been created and studied in order to establish their photophysical and electrochemical characteristics. Then one of these assemblies was formed with chromophores of rhenium, thus creating discrete molecular species containing one to four chromophores. And finally, these assemblies were combined with a cobalt-based catalyst and were tested for hydrogen photoproduction. The latter required the development of a photoreactor which is also described in detail in this thesis

3-Phenyl-6-(2-pyrid­yl)-1,2,4,5-tetra­zine

The title compound, C13H9N5, is the first asymmetric diaryl-1,2,4,5-tetra­zine to be crystallographically characterized. We have been inter­ested in this motif for incorporation into supra­molecular assemblies based on coordination chemistry. The solid state structure shows a centrosymmetric mol­ecule, forcing a positional disorder of the terminal phenyl and pyridyl rings. The mol­ecule is completely planar, unusual for aromatic rings with N atoms in adjacent ortho positions. The stacking observed is very common in diaryl­tetra­zines and is dominated by π stacking [centroid-to-centroid distance between the tetrazine ring and the aromatic ring of an adjacent molecule is 3.6 Å, perpendicular (centroid-to-plane) distance of about 3.3 Å]

Toward Opto-Structural Correlation to Investigate Luminescence Thermometry in an Organometallic Eu(II) Complex

Lanthanide-based luminescent materials have unique properties and are well-studied for many potential applications. In particular, the characteristic 5d → 4f emission of divalent lanthanide ions such as EuII allows for tunability of the emissive properties via modulation of the coordination environment. We report the synthesis and photoluminescence investigation of pentamethylcyclopentadienyleuropium(II) tetrahydroborate bis(tetrahydrofuran) dimer (1), the first example of an organometallic, discrete molecular EuII band-shift luminescence thermometer. Complex 1 exhibits an absolute sensitivity of 8.2 cm–1 K–1 at 320 K, the highest thus far observed for a lanthanide-based band-shift thermometer. Opto-structural correlation via variable-temperature single-crystal X-ray diffraction and fluorescence spectroscopy allows rationalization of the remarkable thermometric luminescence of complex 1 and reveals the significant potential of molecular EuII compounds in luminescence thermometry.</p

Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms

PURPOSE: We develop a practical, iterative algorithm for image-reconstruction in under-sampled tomographic systems, such as digital breast tomosynthesis (DBT). METHOD: The algorithm controls image regularity by minimizing the image total $p$-variation (TpV), a function that reduces to the total variation when $p=1.0$ or the image roughness when $p=2.0$. Constraints on the image, such as image positivity and estimated projection-data tolerance, are enforced by projection onto convex sets (POCS). The fact that the tomographic system is under-sampled translates to the mathematical property that many widely varied resultant volumes may correspond to a given data tolerance. Thus the application of image regularity serves two purposes: (1) reduction of the number of resultant volumes out of those allowed by fixing the data tolerance, finding the minimum image TpV for fixed data tolerance, and (2) traditional regularization, sacrificing data fidelity for higher image regularity. The present algorithm allows for this dual role of image regularity in under-sampled tomography. RESULTS: The proposed image-reconstruction algorithm is applied to three clinical DBT data sets. The DBT cases include one with microcalcifications and two with masses. CONCLUSION: Results indicate that there may be a substantial advantage in using the present image-reconstruction algorithm for microcalcification imaging.Comment: Submitted to Medical Physic

Highly Durable Nanoporous Cu2–xS Films for Efficient Hydrogen Evolution Electrocatalysis under Mild pH Conditions

Copper-based hydrogen evolution electrocatalysts are promising materials to scale-up hydrogen production due to their reported high current densities; however, electrode durability remains a challenge. Here, we report a facile, cost-effective, and scalable synthetic route to produce Cu2–xS electrocatalysts, exhibiting hydrogen evolution rates that increase for ∼1 month of operation. Our Cu2–xS electrodes reach a state-of-the-art performance of ∼400 mA cm–2 at −1 V vs RHE under mild conditions (pH 8.6), with almost 100% Faradaic efficiency for hydrogen evolution. The rise in current density was found to scale with the electrode electrochemically active surface area. The increased performance of our Cu2–xS electrodes correlates with a decrease in the Tafel slope, while analyses by X-ray photoemission spectroscopy, operando X-ray diffraction, and in situ spectroelectrochemistry cooperatively revealed the Cu-centered nature of the catalytically active species. These results allowed us to increase fundamental understanding of heterogeneous electrocatalyst transformation and consequent structure–activity relationship. This facile synthesis of highly durable and efficient Cu2–xS electrocatalysts enables the development of competitive electrodes for hydrogen evolution under mild pH conditions.Funding for open access charge: CRUE-Universitat Jaume IICN2 acknowledges funding from Generalitat de Catalunya 2021SGR00457. This study is part of the Advanced Materials programme and supported by MCIN with funding from European Union NextGenerationEU (PRTR-C17.I1), Generalitat de Catalunya, and the Basque Government (grant IT1591-22). The authors thank the support from the projects (RED2022-134508-T, PID2020-116093RB-C41, PID2020-116093RB-C43, and PID2020-116093RB-C44) funded by MCIN/AEI/10.13039/501100011033/ and the project TED2021-129999A-C33 financed by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR. C.A.M. acknowledges funding from UJI postdoc fellowship POSDOC/2019/20, the Generalitat Valenciana for the APOSTD/2021/251 fellowship, and to MinCiencias Colombia through the Fondo Nacional de Financiamiento para la Ciencia, la Tecnología y la Innovación “Francisco José de Caldas”, call 848-2019. ICN2 is supported by the Severo Ochoa program from Spanish MCIN/AEI (Grant No.: CEX2021-001214-S) and is funded by the CERCA Programme/Generalitat de Catalunya. M.C.S. has received funding from the post-doctoral fellowship Juan de la Cierva Incorporation from MICINN (JCI-2019) and the Severo Ochoa programme. S.B. acknowledges grant RYC-2017-21931 funded by MCIN/AEI/10.13039/501100011033 and by ESF Investing in Your Future, EUR2020-112066 funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR, and UPV/EHU project EHUrOPE19/01. J.R. acknowledges the Czech Science Foundation and funding from PIF outgoing project number 22-18079O

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: 'Worse', 'Stable', or 'Improved' on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between "Worse" and "Improved" outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic ('Consolidation', 'Lung Lesion', 'Pleural effusion' and 'Pneumonia'; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between 'Worse' and 'Improved' cases with a 0.81 (0.74-0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67-0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions

Classical Ising model test for quantum circuits

We exploit a recently constructed mapping between quantum circuits and graphs in order to prove that circuits corresponding to certain planar graphs can be efficiently simulated classically. The proof uses an expression for the Ising model partition function in terms of quadratically signed weight enumerators (QWGTs), which are polynomials that arise naturally in an expansion of quantum circuits in terms of rotations involving Pauli matrices. We combine this expression with a known efficient classical algorithm for the Ising partition function of any planar graph in the absence of an external magnetic field, and the Robertson-Seymour theorem from graph theory. We give as an example a set of quantum circuits with a small number of non-nearest neighbor gates which admit an efficient classical simulation.Comment: 17 pages, 2 figures. v2: main result strengthened by removing oracular settin

Deep learning of chest X‑rays can predict mechanical ventilation outcome in ICU‑admitted COVID‑19 patients

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model

A small dose of whey protein co-ingested with mixed-macronutrient breakfast and lunch meals improves postprandial glycemia and suppresses appetite in men with type 2 diabetes: a randomized controlled trial.

Background: Large doses of whey protein consumed as a preload before single high-glycemic load meals has been shown to improve postprandial glycemia in type 2 diabetes. It is unclear if this effect remains with smaller doses of whey co-ingested at consecutive mixed-macronutrient meals. Moreover, whether hydrolyzed whey offers further benefit under these conditions is unclear. Objective: The aim of this study was to investigate postprandial glycemic and appetite responses after small doses of intact and hydrolyzed whey protein co-ingested with mixed-nutrient breakfast and lunch meals in men with type 2 diabetes. Design: In a randomized, single-blind crossover design, 11 men with type 2 diabetes [mean ± SD age: 54.9 ± 2.3 y; glycated hemoglobin: 6.8% ± 0.3% (51.3 ± 3.4 mmol/mol)] attended the laboratory on 3 mornings and consumed 1) intact whey protein (15 g), 2) hydrolyzed whey protein (15 g), or 3) placebo (control) immediately before mixed-macronutrient breakfast and lunch meals, separated by 3 h. Blood samples were collected periodically and were processed for insulin, intact glucagon-like peptide 1 (GLP-1), gastric inhibitory polypeptide (GIP), leptin, peptide tyrosine tyrosine (PYY3-36), and amino acid concentrations. Interstitial glucose was measured during and for 24 h after each trial. Subjective appetite was assessed with the use of visual analog scales. Results: Total postprandial glycemia area under the curve was reduced by 13% ± 3% after breakfast following the intact whey protein when compared with control (P  0.05). Conclusions: The consumption of a small 15-g dose of intact whey protein immediately before consecutive mixed-macronutrient meals improves postprandial glycemia, stimulates insulin release, and increases satiety in men with type 2 diabetes. This trial was registered at www.clinicialtrials.gov as NCT02903199