Resolution by Unassisted Top3 Points to Template Switch Recombination Intermediates during DNA Replication

The evolutionarily conserved Sgs1/Top3/Rmi1 (STR) complex plays vital roles in DNA replication and repair. One crucial activity of the complex is dissolution of toxic X-shaped recombination intermediates that accumulate during replication of damaged DNA. However, despite several years of study the nature of these X-shaped molecules remains debated. Here we use genetic approaches and two-dimensional gel electrophoresis of genomic DNA to show that Top3, unassisted by Sgs1 and Rmi1, has modest capacities to provide resistance to MMS and to resolve recombination-dependent X-shaped molecules. The X-shaped molecules have structural properties consistent with hemicatenane-related template switch recombination intermediates (Rec-Xs) but not Holliday junction (HJ) intermediates. Consistent with these findings, we demonstrate that purified Top3 can resolve a synthetic Rec-X but not a synthetic double HJ in vitro. We also find that unassisted Top3 does not affect crossing over during double strand break repair, which is known to involve double HJ intermediates, confirming that unassisted Top3 activities are restricted to substrates that are distinct from HJs. These data help illuminate the nature of the X-shaped molecules that accumulate during replication of damaged DNA templates, and also clarify the roles played by Top3 and the STR complex as a whole during the resolution of replication-associated recombination intermediates

Quantitative Imaging Analysis of Non-Small Cell Lung Cancer

Quantitative imaging is a rapidly growing area of interest within the field of bioinformatics and biomarker discovery. Due to the routine nature of medical imaging, there is an abundance of high-quality imaging linked to clinical and genetic data. This data is particularly relevant for cancer patients who receive routine CT imaging for staging and treatment purposes. However, current analysis of tumor imaging is generally limited to two-dimensional diameter measurements and assessment of anatomic disease spread. This conventional tumor-node-metastasis (TNM) staging system stratifies patients to treatment protocols including decisions regarding adjuvant therapy. Recently there have been several studies suggesting that these images contain additional unique information regarding tumor phenotype that can further aid clinical decision-making. In this study I aimed to develop the predictive capability of medical imaging. I employed the principles of quantitative imaging and applied them to patients with non-small cell lung cancer (NSCLC). Quantitative imaging, also termed radiomics, seeks to extract thousands of imaging data points related to tumor shape, size and texture. These data points can potentially be consolidated to develop a tumor signature in the same way that a tumor might contain a genetic signature corresponding to mutational burden. To accomplish this I applied radiomics analyses to patients with early and late stage NSCLC and tested these for correlation with both histopathological data as well as clinical outcomes. Patients with both early and late stage NSCLC were assessed. For locally advanced NSCLC (LA-NSCLC), I analyzed patients treated with preoperative chemoradiation followed by surgical resection. To assess early stage NSCLC, I analyzed patients treated with stereotactic body radiation therapy (SBRT). Quantitative imaging features were extracted from CT imaging obtained prior to chemoradiation and post-chemoradiation prior to surgical resection. For patients who underwent SBRT, quantitative features were extracted from cone-beam CTs (CBCT) at multiple time points during therapy. Univariate and multivariate logistic regression were used to determine association with pathologic response. Concordance-index and Kaplan-Meier analyses were applied to time dependent endpoints of overall survival, locoregional recurrence-free and distant metastasis. In this study, 127 LA-NSCLC patients were identified and treated with preoperative chemoradiation and surgical resection. 99 SBRT patients were identified in a separate aim of this study. Reduction of CT-defined tumor volume (OR 1.06 [1.02-1.09], p=0.002) as continuous variables per percentage point was associated with pathologic complete response (pCR) and locoregional recurrence (LRR). Conventional response assessment determined by diameter (p=0.213) was not associated with pCR or any survival endpoints. Seven texture features on pre-treatment tumor imaging were associated with worse pathologic outcome (AUC 0.61-0.66). Quantitative assessment of lymph node burden demonstrated that pre-treatment and post-treatment volumes are significantly associated with both OS and LRR (CI 0.62-0.72). Textural analyses of these lymph nodes further identified 3 unique pre-treatment and 7 unique post-treatment features significantly associated with either LRR, DM or OS. Finally early volume change showed associated with overall survival in CBCT scans of early NSCLC. Quantitative assessment of NSCLC is thus strongly associated with pathologic response and survival endpoints. In contrast, conventional imaging response assessment was not predictive of pathologic response or survival endpoints. This study demonstrates the novel application of radiomics to lymph node texture, CBCT volume and patients undergoing neoadjuvant therapy for NSCLC. These examples highlight the potential within the rapidly growing field of quantitative imaging to better describe tumor phenotype. These results provide evidence to the growing radioimics literature that there is significant association between imaging, pathology and clinical outcomes. Further exploration will allow for more complete models describing tumor imaging phoentype with clinical outcomes

Case report of tracheobronchial squamous cell carcinoma treated with radiation therapy and concurrent chemotherapy

Tracheobronchial tumors include primary malignant tumors, secondary malignant tumors, and benign tumors. Primary malignant tumors of the trachea are rare, representing only 0.1% to 0.4% of all malignant disease. Squamous cell carcinoma (SCC) and adenoid cystic carcinoma are the most common histological subtypes, making up approximately two-thirds of primary tracheal neoplasms.1 Such tumors have typically been treated with surgical resection and adjuvant radiation therapy (RT; Table 1). Medically inoperable tumors are usually treated with definitive RT, but because of the rarity of these tumors, there are no randomized trials to determine the optimal treatment regimen. A radiation dose of ∼60 Gy has been most commonly reported for external beam RT, with higher doses having significant toxicity of the tracheal and esophageal tissue using historical techniques. In contrast to definitive RT, the use of definitive RT with concurrent chemotherapy for tracheal SCC has been sparingly described in the literature. In this report, we describe our experience with 2 patients at our institution who received definitive RT using modern techniques with concurrent chemotherapy for tracheobronchial SCC

Object Detection in Indian Food Platters using Transfer Learning with YOLOv4

Object detection is a well-known problem in computer vision. Despite this, its usage and pervasiveness in the traditional Indian food dishes has been limited. Particularly, recognizing Indian food dishes present in a single photo is challenging due to three reasons: 1. Lack of annotated Indian food datasets 2. Non-distinct boundaries between the dishes 3. High intra-class variation. We solve these issues by providing a comprehensively labelled Indian food dataset- IndianFood10, which contains 10 food classes that appear frequently in a staple Indian meal and using transfer learning with YOLOv4 object detector model. Our model is able to achieve an overall mAP score of 91.8% and f1-score of 0.90 for our 10 class dataset. We also provide an extension of our 10 class dataset- IndianFood20, which contains 10 more traditional Indian food classes.Comment: 6 pages, 7 figures, 38th IEEE International Conference on Data Engineering, 2022, DECOR Worksho

VinaLigGen: a method to generate LigPlots and retrieval of hydrogen and hydrophobic interactions from protein-ligand complexes

Developments in the field of computational structural biology and with increasing computing speeds have encouraged researchers in studying large compound libraries during the virtual screening. After performing molecular docking, the consideration of vina score in filtering the compounds without collecting the hydrogen bond or hydrophobic interaction between protein and ligand complex leads to missing multiple good lead molecules. The tools used for virtual screening in drug design and discovery studies were previously designed and developed for small datasets. LigPlots were used to generate 2-dimensional (2D) interaction maps of protein-ligand complexes. These maps depict diverse bonds like hydrogen and hydrophobic interactions in varied colors for all ligand conformations within the library. However, handling large numbers of protein-ligand complexes can make this process quite laborious. The development of a tool is strongly required or an implementation of automation to generate all the interaction details has a strong demand. This paper describes an implementation of an automation technique on the executable programs like ligplot.exe, hbplus.exe and hbadd.exe to obtain the 2D interaction map (LigPlots) of the protein and ligand complex (*.ps) and hydrogen bonds and hydrophobic interactions in *.csv format for molecules to be considered for virtual screening by using some sorting & searching algorithms and python’s file handling functions, and it also mentions the program’s limitations and availability of the program. The program can be found on github. Communicated by Ramaswamy H. Sarma</p