Scaffolding for Optimal Challenge in K–12 Problem-Based Learning

Establishing optimal challenge enhances intrinsic motivation, interest, and the probability of success in the learning activity. In K–12 problem-based learning (PBL), students may struggle to address associated tasks that are beyond their current ability levels. This paper suggested learner-centered scaffolding systems (LSS) to improve K–12 students’ perception of optimal challenge by addressing their learning issues in PBL. LSS enhances students’ experience in autonomy and competence by providing multiple types of scaffolding in accordance with students’ different needs and difficulties in PBL. Students can control the nature and frequency of scaffolding by themselves according to their needs and ability, and it plays a role in improving their self-directed learning skills. Last, peer scaffolding between students with similar abilities satisfies students’ needs for relatedness

Stereocontrolled enantioselective total synthesis of the [2+2] quadrigemine alkaloids.

A unified strategy for enantioselective total synthesis of all stereoisomers of the 2+2 family of quadrigemine alkaloids is reported. In this approach, two enantioselective intramolecular Heck reactions are carried out at the same time on precursors fashioned in four steps from either meso- or (+)-chimonanthine to form the two critical quaternary carbons of the peripheral cyclotryptamine rings of these products. Useful levels of catalyst control are realized in either desymmetrizing a meso precursor or controlling diastereoselectivity in elaborating C2-symmetic intermediates. None of the synthetic quadrigemines are identical with alkaloids isolated previously and referred to as quadrigemines A and E. In addition, we report improvements in our previous total syntheses of (+)- or (-)-quadrigemine C that shortened the synthetic sequence to 10 steps and provided these products in 2.2% overall yield from tryptamine

Genetic regulation of mouse liver metabolite levels.

We profiled and analyzed 283 metabolites representing eight major classes of molecules including Lipids, Carbohydrates, Amino Acids, Peptides, Xenobiotics, Vitamins and Cofactors, Energy Metabolism, and Nucleotides in mouse liver of 104 inbred and recombinant inbred strains. We find that metabolites exhibit a wide range of variation, as has been previously observed with metabolites in blood serum. Using genome-wide association analysis, we mapped 40% of the quantified metabolites to at least one locus in the genome and for 75% of the loci mapped we identified at least one candidate gene by local expression QTL analysis of the transcripts. Moreover, we validated 2 of 3 of the significant loci examined by adenoviral overexpression of the genes in mice. In our GWAS results, we find that at significant loci the peak markers explained on average between 20 and 40% of variation in the metabolites. Moreover, 39% of loci found to be regulating liver metabolites in mice were also found in human GWAS results for serum metabolites, providing support for similarity in genetic regulation of metabolites between mice and human. We also integrated the metabolomic data with transcriptomic and clinical phenotypic data to evaluate the extent of co-variation across various biological scales

Fluorescent humanized anti-CEA antibody specifically labels metastatic pancreatic cancer in a patient-derived orthotopic xenograft (PDOX) mouse model.

Pancreatic cancer is a highly lethal disease in part due to incomplete tumor resection. Targeting by tumor-specific antibodies conjugated with a fluorescent label can result in selective labeling of cancer in vivo for surgical navigation. In the present study, we describe a patient-derived orthotopic xenograft model of pancreatic cancer that recapitulated the disease on a gross and microscopic level, along with physiologic clinical manifestations. We additionally show that the use of an anti-CEA antibody conjugated to the near-infrared (NIR) fluorescent dye, IRDye800CW, can selectively highlight the pancreatic cancer and its metastases in this model with a tumor-to-background ratio of 3.5 (SEM 0.9). The present results demonstrate the clinical potential of this labeling technique for fluorescence-guided surgery of pancreatic cancer

Boosting protein stability with the computational design of β-sheet surfaces: Computational Design of β-Sheet Surfaces

β‐sheets often have one face packed against the core of the protein and the other facing solvent. Mutational studies have indicated that the solvent‐facing residues can contribute significantly to protein stability, and that the preferred amino acid at each sequence position is dependent on the precise structure of the protein backbone and the identity of the neighboring amino acids. This suggests that the most advantageous methods for designing β‐sheet surfaces will be approaches that take into account the multiple energetic factors at play including side chain rotamer preferences, van der Waals forces, electrostatics, and desolvation effects. Here, we show that the protein design software Rosetta, which models these energetic factors, can be used to dramatically increase protein stability by optimizing interactions on the surfaces of small β‐sheet proteins. Two design variants of the β‐sandwich protein from tenascin were made with 7 and 14 mutations respectively on its β‐sheet surfaces. These changes raised the thermal midpoint for unfolding from 45°C to 64°C and 74°C. Additionally, we tested an empirical approach based on increasing the number of potential salt bridges on the surfaces of the β‐sheets. This was not a robust strategy for increasing stability, as three of the four variants tested were unfolded

A Bayesian Network Meta-Analysis to Synthesize the Influence of Contexts of Scaffolding Use on Cognitive Outcomes in STEM Education

Computer-based scaffolding provides temporary support that enables students to participate in and become more proficient at complex skills like problem solving, argumentation, and evaluation. While meta-analyses have addressed between-subject differences on cognitive outcomes resulting from scaffolding, none has addressed within-subject gains. This leaves much quantitative scaffolding literature not covered by existing meta-analyses. To address this gap, this study used Bayesian network meta-analysis to synthesize within-subjects (pre–post) differences resulting from scaffolding in 56 studies. We generated the posterior distribution using 20,000 Markov Chain Monte Carlo samples. Scaffolding has a consistently strong effect across student populations, STEM (science, technology, engineering, and mathematics) disciplines, and assessment levels, and a strong effect when used with most problem-centered instructional models (exception: inquiry-based learning and modeling visualization) and educational levels (exception: secondary education). Results also indicate some promising areas for future scaffolding research, including scaffolding among students with learning disabilities, for whom the effect size was particularly large (ḡ = 3.13)