Computational Reverse Engineering Analysis of Scattering Experiments Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D)

Characterization of structural diversity within soft materials is key for engineering new materials for various applications. Small-angle scattering (SAS) is a widely used characterization technique that provides structural information in soft materials at varying length scales and typically outputs scattered intensity I(q) as a function of the scattered wavevector represented by its magnitude q and azimuthal angle {\theta}. While isotropic structures can be interpreted from azimuthally averaged 1D SAS profile, to understand anisotropic spatial arrangements, one has to interpret the 2D SAS profile, I(q,{\theta}). In this paper, we present a new method called CREASE-2D that interprets I(q,{\theta}) as is and outputs the relevant structural features. CREASE-2D is an extension of the 'computational reverse engineering analysis for scatting experiments' (CREASE) method that has been used successfully to analyze 1D SAS profiles for a variety of soft materials. CREASE uses a genetic algorithm for optimization and a surrogate machine learning (ML) model for fast calculation of 1D 'computed' scattering profiles that are then compared to the experimental 1D scattering profiles during optimization. In CREASE-2D, which goes beyond CREASE in interpretting 2D scattering profiles, we use XGBoost as the surrogate ML model to relate structural features to the I(q,{\theta}) profile. The CREASE-2D workflow identifies the structural features whose computed I(q,{\theta}) profiles match the input experimental I(q,{\theta}). We test the performance of CREASE-2D by using as input a variety of in silico 2D SAS profiles with known structural features and demonstrate that CREASE-2D converges towards their correct structural features. We expect this method will be valuable for materials' researchers who need direct interpretation of 2D scattering profiles to explore structural anisotropy.Comment: 14 pages, 5 figures, supporting information include

High-Throughput Label-Free Isolation of Heterogeneous Circulating Tumor Cells and CTC Clusters from Non-Small-Cell Lung Cancer Patients.

(1) Background: Circulating tumor cell (CTC) clusters are emerging as clinically significant harbingers of metastases in solid organ cancers. Prior to engaging these CTC clusters in animal models of metastases, it is imperative for technology to identify them with high sensitivity. These clusters often present heterogeneous surface markers and current methods for isolation of clusters may fall short. (2) Methods: We applied an inertial microfluidic Labyrinth device for high-throughput, biomarker-independent, size-based isolation of CTCs/CTC clusters from patients with metastatic non-small-cell lung cancer (NSCLC). (3) Results: Using Labyrinth, CTCs (PanCK+/DAPI+/CD45-) were isolated from patients (n = 25). Heterogeneous CTC populations, including CTCs expressing epithelial (EpCAM), mesenchymal (Vimentin) or both markers were detected. CTCs were isolated from 100% of patients (417 +/- 1023 CTCs/mL). EpCAM- CTCs were significantly greater than EpCAM+ CTCs. Cell clusters of \u3e/=2 CTCs were observed in 96% of patients-of which, 75% were EpCAM-. CTCs revealed identical genetic aberrations as the primary tumor for RET, ROS1, and ALK genes using fluorescence in situ hybridization (FISH) analysis. (4) Conclusions: The Labyrinth device recovered heterogeneous CTCs in 100% and CTC clusters in 96% of patients with metastatic NSCLC. The majority of recovered CTCs/clusters were EpCAM-, suggesting that these would have been missed using traditional antibody-based capture methods

UNT Libraries Student Snapshots Symposium

This poster explores data gathered from the UNT Libraries' reference analytics from questions submitted to the libraries. It was presented at the UNT Libraries' 2023 Student Snapshots Symposium held in Denton, Texas

Data Analysis for Fraud Detection in Finance

Credit card use is not always the best way to use for payments, but the most demonstrable payment mode is through the credit card for both offline as well as for online payments, which can result in deficit of funds. As the online shopping is booming it helps in rendering the cashless payment modes. It can be used at shopping's, paying rent, paying utilities bill, internet bill, travel and transportation, entertainment, food. Using for all these things there is a chance of fraud transactions for a credit card, hence there is more risk. There are many types of fraudulent detections most of the banks and institutions are preferring fraud detection applications.it has become very hard to find out the fraud detections, After the transaction is done there is a chance of detecting fraudulent transactions in the manual business processing system. In real time the bunco transactions are done with real transactions, but it seems not to be sufficient for detecting . Machine learning and data science both are playing a very important role in identifying the fraud detections. This study uses data science and machine learning for detecting the fraud detection to demonstrate various modellings. The problem enables the transactions of the previously done transaction data

A 22-year-old woman with systemic lupus erythematosus develops cardiac tamponade

Introduction Systemic lupus erythematosus (SLE) is a common cause of pericardial effusion and acute pericarditis, but very rarely it can cause cardiac tamponade.1 We describe the case of a young female with SLE who developed cardiac tamponade after finishing treatment for acute pericarditis with a small pericardial effusion

AN IMPROVED BIT LOADING TECHNIQUE FOR ENHANCED ENERGY EFFICIENCY IN NEXT GENERATION VOICE/VIDEO APPLICATIONS

Multi input multi output (MIMO) and orthogonal frequency division multiplexing (OFDM) are the key techniques for the future wireless communication systems. Previous research in the above areas mainly concentrated on spectral efficiency improvement and very limited work has been done in terms of energy efficient transmission. In addition to spectral efficiency improvement, energy efficiency improvement has become an important research because of the slow progressing nature of the battery technology. Since most of the user equipments (UE) rely on battery, the energy required to transmit the target bits should be minimized to avoid quick battery drain. The frequency selective fading nature of the wireless channel reduces the spectral and energy efficiency of OFDM based systems. Dynamic bit loading (DBL) is one of the suitable solution to improve the spectral and energy efficiency of OFDM system in frequency selective fading environment. Simple dynamic bit loading (SDBL) algorithm is identified to offer better energy efficiency with less system complexity. It is well suited for fixed data rate voice/video applications. When the number of target bits are very much larger than the available subcarriers, the conventional single input single output (SISO)-SDBL scheme offers high bit error rate (BER) and needs large transmit energy. To improve bit error performance we combine space frequency block codes (SFBC) with SDBL, where the adaptations are done in both frequency and spatial domain. To improve the quality of service (QoS) further, optimal transmit antenna selection (OTAS) scheme is also combined with SFBC-SDBL scheme. The simulation results prove that the proposed schemes offer better QoS when compared to the conventional SISOSDBL scheme