Bridging Physics and Biology Teaching through Modeling

As the frontiers of biology become increasingly interdisciplinary, the physics education community has engaged in ongoing efforts to make physics classes more relevant to life sciences majors. These efforts are complicated by the many apparent differences between these fields, including the types of systems that each studies, the behavior of those systems, the kinds of measurements that each makes, and the role of mathematics in each field. Nonetheless, physics and biology are both sciences that rely on observations and measurements to construct models of the natural world. In the present theoretical article, we propose that efforts to bridge the teaching of these two disciplines must emphasize shared scientific practices, particularly scientific modeling. We define modeling using language common to both disciplines and highlight how an understanding of the modeling process can help reconcile apparent differences between the teaching of physics and biology. We elaborate how models can be used for explanatory, predictive, and functional purposes and present common models from each discipline demonstrating key modeling principles. By framing interdisciplinary teaching in the context of modeling, we aim to bridge physics and biology teaching and to equip students with modeling competencies applicable across any scientific discipline.Comment: 10 pages, 2 figures, 3 table

Spitzer UltRa Faint SUrvey Program (SURFS UP). II. IRAC-Detected Lyman-Break Galaxies at 6 < z < 10 Behind Strong-Lensing Clusters

We study the stellar population properties of the IRAC-detected $6 \lesssim z \lesssim 10$ galaxy candidates from the Spitzer UltRa Faint SUrvey Program (SURFS UP). Using the Lyman Break selection technique, we find a total of 16 new galaxy candidates at $6 \lesssim z \lesssim 10$ with $S/N \geq 3$ in at least one of the IRAC $3.6\mu$m and $4.5\mu$m bands. According to the best mass models available for the surveyed galaxy clusters, these IRAC-detected galaxy candidates are magnified by factors of $\sim 1.2$--$5.5$. We find that the IRAC-detected $6 \lesssim z \lesssim 10$ sample is likely not a homogeneous galaxy population: some are relatively massive (stellar mass as high as $4 \times 10^9\,M_{\odot}$) and evolved (age $\lesssim 500$ Myr) galaxies, while others are less massive ($M_{\text{stellar}}\sim 10^8\,M_{\odot}$) and very young ($\sim 10$ Myr) galaxies with strong nebular emission lines that boost their rest-frame optical fluxes. We identify two Ly$\alpha$ emitters in our sample from the Keck DEIMOS spectra, one at $z_{\text{Ly}\alpha}=6.76$ (in RXJ1347) and one at $z_{\text{Ly}\alpha}=6.32$ (in MACS0454). We show that IRAC $[3.6]-[4.5]$ color, when combined with photometric redshift, can be used to identify galaxies likely with strong nebular emission lines within certain redshift windows.Comment: ApJ in pres

Effects of Acrylic Restorations on the Periodontium of Monkeys

Replanted teeth that had an acrylic restoration in the middle third of their roots were studied from three days to six months after grafting. The study revealed that the acrylic obturator elicited epithelial proliferation, fibrous encapsulation, moderate chronic inflammation, and adjacent alveolar bone loss.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68162/2/10.1177_00220345720510052201.pd

Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models

We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, K(trans), plasma volume fraction, v(p), and extravascular, extracellular space, v(e), directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, v(p), K(trans), and v(e), respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches

Проблеми вищої освіти в Україні в сучасних умовах

Most experts agree that it is too early to say how quantum computers will eventually be built, and several nanoscale solid-state schemes are being implemented in a range of materials. Nanofabricated quantum dots can be made in designer configurations, with established technology for controlling interactions and for reading out results. Epitaxial quantum dots can be grown in vertical arrays in semiconductors, and ultrafast optical techniques are available for controlling and measuring their excitations. Single-walled carbon nanotubes can be used for molecular self-assembly of endohedral fullerenes, which can embody quantum information in the electron spin. The challenges of individual addressing in such tiny structures could rapidly become intractable with increasing numbers of qubits, but these schemes are amenable to global addressing methods for computation

Breast cancer family history and allele-specific DNA methylation in the Legacy Girls Study

Family history, a well-established risk factor for breast cancer, can have both genetic and environmental contributions. Shared environment in families as well as epigenetic changes that also may be influenced by shared genetics and environment may also explain familial clustering of cancers. Epigenetic regulation, such as DNA methylation, can change the activity of a DNA segment without a change in the sequence; environmental exposures experienced across the life course can induce such changes. However, genetic-epigenetic interactions, detected as methylation quantitative trait loci (mQTLs; a.k.a. meQTLs) and haplotype-dependent allele-specific methylation (hap-ASM), can also contribute to inter-individual differences in DNA methylation patterns. To identify differentially methylated regions (DMRs) associated with breast cancer susceptibility, we examined differences in white blood cell DNA methylation in 29 candidate genes in 426 girls (ages 6-13 years) from the LEGACY Girls Study, 239 with and 187 without a breast cancer family history (BCFH). We measured methylation by targeted massively parallel bisulfite sequencing (bis-seq) and observed BCFH DMRs in two genes: ESR1 (Δ4.9%, P = 0.003) and SEC16B (Δ3.6%, P = 0.026), each of which has been previously implicated in breast cancer susceptibility and pubertal development. These DMRs showed high inter-individual variability in methylation, suggesting the presence of mQTLs/hap-ASM. Using single nucleotide polymorphisms data in the bis-seq amplicon, we found strong hap-ASM in SEC16B (with allele specific-differences ranging from 42% to 74%). These findings suggest that differential methylation in genes relevant to breast cancer susceptibility may be present early in life, and that inherited genetic factors underlie some of these epigenetic differences

Deconstruction of a Metastatic Tumor Microenvironment Reveals a Common Matrix Response in Human Cancers.

We have profiled, for the first time, an evolving human metastatic microenvironment by measuring gene expression, matrisome proteomics, cytokine and chemokine levels, cellularity, extracellular matrix organization, and biomechanical properties, all on the same sample. Using biopsies of high-grade serous ovarian cancer metastases that ranged from minimal to extensive disease, we show how nonmalignant cell densities and cytokine networks evolve with disease progression. Multivariate integration of the different components allowed us to define, for the first time, gene and protein profiles that predict extent of disease and tissue stiffness, while also revealing the complexity and dynamic nature of matrisome remodeling during development of metastases. Although we studied a single metastatic site from one human malignancy, a pattern of expression of 22 matrisome genes distinguished patients with a shorter overall survival in ovarian and 12 other primary solid cancers, suggesting that there may be a common matrix response to human cancer.Significance: Conducting multilevel analysis with data integration on biopsies with a range of disease involvement identifies important features of the evolving tumor microenvironment. The data suggest that despite the large spectrum of genomic alterations, some human malignancies may have a common and potentially targetable matrix response that influences the course of disease. Cancer Discov; 8(3); 304-19. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 253