Systematic study of Ga1−x_{1-x}Inx_xAs self-assembled quantum wires with different interfacial strain relaxation

A systematic theoretical study of the electronic and optical properties of Ga$_{1-x}$In$_x$As self-assembled quantum-wires (QWR's) made of short-period superlattices (SPS) with strain-induced lateral ordering is presented. The theory is based on the effective bond-orbital model (EBOM) combined with a valence-force field (VFF) model. Valence-band anisotropy, band mixing, and effects due to local strain distribution at the atomistic level are all taken into account. Several structure models with varying degrees of alloy mixing for lateral modulation are considered. A valence force field model is used to find the equilibrium atomic positions in the QWR structure by minimizing the lattice energy. The strain tensor at each atomic (In or Ga) site is then obtained and included in the calculation of electronic states and optical properties. It is found that different local arrangement of atoms leads to very different strain distribution, which in turn alters the optical properties. In particular, we found that in model structures with thick capping layer the electron and hole are confined in the Ga-rich region and the optical anisotropy can be reversed due to the variation of lateral alloying mixing, while for model structures with thin capping layer the electron and hole are confined in the In-rich region, and the optical anisotropy is much less sensitive to the lateral alloy mixing.Comment: 23 pages, and 8 figure

Extraction of Text from Optic Nerve Optical Coherence Tomography Reports

Purpose: The purpose of this study was to develop and evaluate rule-based algorithms to enhance the extraction of text data, including retinal nerve fiber layer (RNFL) values and other ganglion cell count (GCC) data, from Zeiss Cirrus optical coherence tomography (OCT) scan reports. Methods: DICOM files that contained encapsulated PDF reports with RNFL or Ganglion Cell in their document titles were identified from a clinical imaging repository at a single academic ophthalmic center. PDF reports were then converted into image files and processed using the PaddleOCR Python package for optical character recognition. Rule-based algorithms were designed and iteratively optimized for improved performance in extracting RNFL and GCC data. Evaluation of the algorithms was conducted through manual review of a set of RNFL and GCC reports. Results: The developed algorithms demonstrated high precision in extracting data from both RNFL and GCC scans. Precision was slightly better for the right eye in RNFL extraction (OD: 0.9803 vs. OS: 0.9046), and for the left eye in GCC extraction (OD: 0.9567 vs. OS: 0.9677). Some values presented more challenges in extraction, particularly clock hours 5 and 6 for RNFL thickness, and signal strength for GCC. Conclusions: A customized optical character recognition algorithm can identify numeric results from optical coherence scan reports with high precision. Automated processing of PDF reports can greatly reduce the time to extract OCT results on a large scale

CD24 Expression and differential resistance to chemotherapy in triple-negative breast cancer.

Breast cancer (BC) is a leading cause of cancer-related death in women. Adjuvant systemic chemotherapies are effective in reducing risks of recurrence and have contributed to reduced BC mortality. Although targeted adjuvant treatments determined by biomarkers for endocrine and HER2-directed therapies are largely successful, predicting clinical benefit from chemotherapy is more challenging. Drug resistance is a major reason for treatment failures. Efforts are ongoing to find biomarkers to select patients most likely to benefit from chemotherapy. Importantly, cell surface biomarkers CD44+/CD24- are linked to drug resistance in some reports, yet underlying mechanisms are largely unknown. This study focused on the potential role of CD24 expression in resistance to either docetaxel or doxorubicin in part by the use of triple-negative BC (TNBC) tissue microarrays. In vitro assays were also done to assess changes in CD24 expression and differential drug susceptibility after chemotherapy. Further, mouse tumor xenograft studies were done to confirm in vitro findings. Overall, the results show that patients with CD24-positive TNBC had significantly worse overall survival and disease-free survival after taxane-based treatment. Also, in vitro cell studies show that CD44+/CD24+/high cells are more resistant to docetaxel, while CD44+/CD24-/low cells are resistant to doxorubicin. Both in vitro and in vivo studies show that cells with CD24-knockdown are more sensitive to docetaxel, while CD24-overexpressing cells are more sensitive to doxorubicin. Further, mechanistic studies indicate that Bcl-2 and TGF-βR1 signaling via ATM-NDRG2 pathways regulate CD24. Hence, CD24 may be a biomarker to select chemotherapeutics and a target to overcome TNBC drug resistance

Deep learning based single image super-resolution : a survey

Single image super-resolution has attracted increasing attention and has a wide range of applications in satellite imaging, medical imaging, computer vision, security surveillance imaging, remote sensing, objection detection, and recognition. Recently, deep learning techniques have emerged and blossomed, producing “the state-of-the-art” in many domains. Due to their capability in feature extraction and mapping, it is very helpful to predict high-frequency details lost in low-resolution images. In this paper, we give an overview of recent advances in deep learning-based models and methods that have been applied to single image super-resolution tasks. We also summarize, compare and discuss various models from the past and present for comprehensive understanding and finally provide open problems and possible directions for future research

The Dean’s Racial Justice Curriculum Challenge

This Work in Progress paper will present the College of Engineering Dean’s Racial Justice Curriculum Challenge. This challenge tasks all faculty in the college to use their engineering problem-solving skills to develop creative ways to incorporate issues of diversity, equity, inclusion, and racial justice in every class we teach. The challenge was inspired by our students, who requested a greater connection between the technical content of classes and real world issues, in particular the role engineers play in either fostering inclusive solutions or contributing to the propagation of inequities. The intent is to engage faculty in the development of new curriculum, and success was measured by the level of engagement, and featured direct student feedback into the curriculum ideas. Starting in 2020-2021 academic year, we challenged every faculty member to contribute to the Dean’s Curriculum Challenge. Each lesson plan was reviewed by a team of students, and at least one was highlighted each week. We received 67 lesson plans from 45 faculty members, impacting 52 courses. Several courses included more than one racial justice themed lesson during the semester. Faculty participation rates were higher in Fall 2020, but varied across departments: 33% of biomedical engineering (BME), 7% of civil engineering (CE), 61% of chemical engineering (CHE), 15% of electrical and computer engineering (ECE), and 26% of mechanical and industrial engineering (MIE). In Spring 2021, 83% of BME, 14% of CE, 26% of CHE, 5% of MIE, and 33% of our Junior Year Writing faculty participated. In total, 3 freshmen, 10 sophomore, 12 junior, 8 senior, 12 senior/graduate, and 9 graduate level classes were impacted. In Fall 2021 we added a second challenge faculty could contribute to: the Inclusive Design challenge. Thus far, we have had 5 submissions from 3 faculty members and 1 graduate student teaching fellow. In addition, the challenge inspired individual department “brainstorming sessions” to discuss pedagogy and best practices for introducing these topics into a variety of class types. This paper will describe the lessons learned as the Dean’s Curriculum Challenge has been implemented as well as plans for sustaining and further supporting the challenge. This will include types of lesson plans, activities, and class discussions that were introduced. Once the program has been established, data will be collected from faculty on what they found effective and whether they continued these or other related activities in future semesters. In the 2021-2022 academic year, several highlighted submissions were presented by the faculty to a wider audience within the college

Sagittal abdominal diameter and its socioeconomic correlates: perspective of sex differences

Background: Sagittal abdominal diameter (SAD) is an anthropometric index associated with visceral adiposity. It remains unclear whether SAD and its socio-economic correlates differ in women and men, which limits the epidemiological and clinical applications of the SAD measurement. The aims of this study are to examine the sex differences in SAD and its socio-economic correlates. Methods: A complex stratified multistage clustered sampling design was used to select 6975 men and 7079 women aged 18 years or more from the National Health Nutrition and Examination Survey 2011-2016, representative of the US civilian non-institutionalized population. SAD was measured in accordance to the standard protocols using a two-arm abdominal caliper. The sex differences in SAD and its socio-economic correlates were evaluated by performing weighted independent t tests and weighted multiple regression. Results: SAD was lower in women than in men in the entire sample, as well as in all the subgroups characterized by age, race, birth place, household income, and body mass index except for non-Hispanic blacks and those with household income < $20,000. Adjusted for other characteristics, age, birth place, household income, and body mass index were associated with SAD in both women and men. Black women were associated with higher SAD then white women (p < .0001), and Hispanic and Asian men were associated with lower SAD than white men (both p < .01). Women born in other countries were more likely to have lower SAD than women born in the US (p < .0001), and so were men (p = .0118). Both women and men with a household income of <$75,000 had higher SAD than those with an income of over $75,000. The associations of age, race, and household income with SAD differed in women and men. Conclusion: SAD is lower in women than in men, in the general population as well as in the most socio-economic subgroups. While socio-economic correlates of SAD are similar in women and men, the associations of age, race, and household income with SAD vary across sex