Physical Sciences and Mathematics Commons^™

Emulate Randomized Clinical Trials Using Heterogeneous Treatment Effect Estimation For Personalized Treatments: Methodology Review And Benchmark, Yaobin Ling, Pulakesh Upadhyaya, Luyao Chen, Xiaoqian Jiang, Yejin Kim

Faculty, Staff and Student Publications

Big data and (deep) machine learning have been ambitious tools in digital medicine, but these tools focus mainly on association. Intervention in medicine is about the causal effects. The average treatment effect has long been studied as a measure of causal effect, assuming that all populations have the same effect size. However, no "one-size-fits-all" treatment seems to work in some complex diseases. Treatment effects may vary by patient. Estimating heterogeneous treatment effects (HTE) may have a high impact on developing personalized treatment. Lots of advanced machine learning models for estimating HTE have emerged in recent years, but there has been …

Covidanno, Covid-19 Annotation In Human, Yuzhou Feng, Mengyuan Yang, Zhiwei Fan, Weiling Zhao, Pora Kim, Xiaobo Zhou

Faculty, Staff and Student Publications

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of coronavirus disease 19 (COVID-19), has caused a global health crisis. Despite ongoing efforts to treat patients, there is no universal prevention or cure available. One of the feasible approaches will be identifying the key genes from SARS-CoV-2-infected cells. SARS-CoV-2-infected in vitro model, allows easy control of the experimental conditions, obtaining reproducible results, and monitoring of infection progression. Currently, accumulating RNA-seq data from SARS-CoV-2 in vitro models urgently needs systematic translation and interpretation. To fill this gap, we built COVIDanno, COVID-19 annotation in humans, available at http://biomedbdc.wchscu.cn/COVIDanno/. The aim …

Syllable-Pbwt For Space-Efficient Haplotype Long-Match Query, Victor Wang, Ardalan Naseri, Shaojie Zhang, Degui Zhi

Faculty, Staff and Student Publications

MOTIVATION: The positional Burrows-Wheeler transform (PBWT) has led to tremendous strides in haplotype matching on biobank-scale data. For genetic genealogical search, PBWT-based methods have optimized the asymptotic runtime of finding long matches between a query haplotype and a predefined panel of haplotypes. However, to enable fast query searches, the full-sized panel and PBWT data structures must be kept in memory, preventing existing algorithms from scaling up to modern biobank panels consisting of millions of haplotypes. In this work, we propose a space-efficient variation of PBWT named Syllable-PBWT, which divides every haplotype into syllables, builds the PBWT positional prefix arrays on …

Text Classification Of Cancer Clinical Trial Eligibility Criteria, Yumeng Yang, Soumya Jayaraj, Ethan Ludmir, Kirk Roberts

Faculty, Staff and Student Publications

Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility are stated in natural language. A potential solution to this problem is to employ text classification methods for common types of eligibility criteria. In this study, we focus on seven common exclusion criteria in cancer trials: prior malignancy, human immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness, drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase III cancer trials with these exclusions annotated at the trial level. We experiment with common transformer models as well as a new pre-trained …

Use Gpt-J Prompt Generation With Roberta For Ner Models On Diagnosis Extraction Of Periodontal Diagnosis From Electronic Dental Records, Yao-Shun Chuang, Xiaoqian Jiang, Chun-Teh Lee, Ryan Brandon, Duong Tran, Oluwabunmi Tokede, Muhammad F Walji

Faculty, Staff and Student Publications

This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as to generate the seed and further fed to the RoBERTa model with the spaCy package. In the direct test, a lower ratio of negative examples with higher numbers of examples in prompt achieved the best results with a F1 score of 0.72. The performance revealed consistency, 0.92-0.97 in the F1 score, in all settings after training with the RoBERTa model. …

A Novel Nih Research Grant Recommender Using Bert, Jie Zhu, Braja Gopal Patra, Hulin Wu, Ashraf Yaseen

Faculty, Staff and Student Publications

Research grants are important for researchers to sustain a good position in academia. There are many grant opportunities available from different funding agencies. However, finding relevant grant announcements is challenging and time-consuming for researchers. To resolve the problem, we proposed a grant announcements recommendation system for the National Institute of Health (NIH) grants using researchers' publications. We formulated the recommendation as a classification problem and proposed a recommender using state-of-the-art deep learning techniques: i.e. Bidirectional Encoder Representations from Transformers (BERT), to capture intrinsic, non-linear relationship between researchers' publications and grants announcements. Internal and external evaluations were conducted to assess the …

Data Mining Pipeline For Covid-19 Vaccine Safety Analysis Using A Large Electronic Health Record, Yan Huang, Xiaojin Li, Deepa Dongarwar, Hulin Wu, Guo-Qiang Zhang

Faculty, Staff and Student Publications

We developed a novel data mining pipeline that automatically extracts potential COVID-19 vaccine-related adverse events from a large Electronic Health Record (EHR) dataset. We applied this pipeline to Optum® de-identified COVID-19 EHR dataset containing COVID-19 vaccine records between December 11, 2020 and January 20, 2022. We compared post-vaccination diagnoses between the COVID-19 vaccine group and the influenza vaccine group among 553,682 individuals without COVID-19 infection. We extracted 1,414 ICD-10 diagnosis categories (first three ICD10 digits) within 180 days after the first dose of the COVID-19 vaccine. We then ranked the diagnosis codes using the adverse event rates and adjusted odds …

Machine Learning-Based Prediction Of Acute Mortality In Emergency Department Patients Using Twelve-Lead Electrocardiogram, Po-Cheng Chang, Zhi-Yong Liu, Yu-Chang Huang, Yu-Chun Hsu, Jung-Sheng Chen, Ching-Heng Lin, Richard Tsai, Chung-Chuan Chou, Ming-Shien Wen, Hung-Ta Wo, Wen-Chen Lee, Hao-Tien Liu, Chun-Chieh Wang, Chang-Fu Kuo

Faculty, Staff and Student Publications

BACKGROUND: The risk of mortality is relatively high among patients who visit the emergency department (ED), and stratifying patients at high risk can help improve medical care. This study aimed to create a machine-learning model that utilizes the standard 12-lead ECG to forecast acute mortality risk in ED patients.

METHODS: The database included patients who visited the EDs and underwent standard 12-lead ECG between October 2007 and December 2017. A convolutional neural network (CNN) ECG model was developed to classify survival and mortality using 12-lead ECG tracings acquired from 345,593 ED patients. For machine learning model development, the patients were …

Artificial Intelligence-Enabled Electrocardiographic Screening For Left Ventricular Systolic Dysfunction And Mortality Risk Prediction, Yu-Chang Huang, Yu-Chun Hsu, Zhi-Yong Liu, Ching-Heng Lin, Richard Tsai, Jung-Sheng Chen, Po-Cheng Chang, Hao-Tien Liu, Wen-Chen Lee, Hung-Ta Wo, Chung-Chuan Chou, Chun-Chieh Wang, Ming-Shien Wen, Chang-Fu Kuo

Faculty, Staff and Student Publications

BACKGROUND: Left ventricular systolic dysfunction (LVSD) characterized by a reduced left ventricular ejection fraction (LVEF) is associated with adverse patient outcomes. We aimed to build a deep neural network (DNN)-based model using standard 12-lead electrocardiogram (ECG) to screen for LVSD and stratify patient prognosis.

METHODS: This retrospective chart review study was conducted using data from consecutive adults who underwent ECG examinations at Chang Gung Memorial Hospital in Taiwan between October 2007 and December 2019. DNN models were developed to recognize LVSD, defined as LVEF

RESULTS: The mean age of patients in the testing dataset was 63.7 ± 16.3 years (46.3% …

Efficient Federated Kinship Relationship Identification, Xinyue Wang, Leonard Dervishi, Wentao Li, Xiaoqian Jiang, Erman Ayday, Jaideep Vaidya

Faculty, Staff and Student Publications

Kinship relationship estimation plays a significant role in today's genome studies. Since genetic data are mostly stored and protected in different silos, retrieving the desirable kinship relationships across federated data warehouses is a non-trivial problem. The ability to identify and connect related individuals is important for both research and clinical applications. In this work, we propose a new privacy-preserving kinship relationship estimation framework: Incremental Update Kinship Identification (INK). The proposed framework includes three key components that allow us to control the balance between privacy and accuracy (of kinship estimation): an incremental process coupled with the use of auxiliary information and …

Sensitive Data Detection With High-Throughput Machine Learning Models In Electrical Health Records, Kai Zhang, Xiaoqian Jiang

Faculty, Staff and Student Publications

In the era of big data, there is an increasing need for healthcare providers, communities, and researchers to share data and collaborate to improve health outcomes, generate valuable insights, and advance research. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law designed to protect sensitive health information by defining regulations for protected health information (PHI). However, it does not provide efficient tools for detecting or removing PHI before data sharing. One of the challenges in this area of research is the heterogeneous nature of PHI fields in data across different parties. This variability makes rule-based …

Towards Fair Patient-Trial Matching Via Patient-Criterion Level Fairness Constraint, Chia-Yuan Chang, Jiayi Yuan, Sirui Ding, Qiaoyu Tan, Kai Zhang, Xiaoqian Jiang, Xia Hu, Na Zou

Faculty, Staff and Student Publications

Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria. Recent studies have shown that these frameworks outperform earlier approaches. However, deep learning models may raise fairness issues in patient-trial matching when certain sensitive groups of individuals are underrepresented in clinical trials, leading to incomplete or inaccurate data and potential harm. To …

Large Language Models For Healthcare Data Augmentation: An Example On Patient-Trial Matching, Jiayi Yuan, Ruixiang Tang, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges such as data standardization, ethical considerations, and a lack of interoperability between Electronic Health Records (EHRs) and clinical trial criteria. In this paper, we explore the potential of large language models (LLMs) to address these challenges by leveraging their advanced natural language generation capabilities to improve compatibility between EHRs and clinical trial descriptions. We propose an innovative privacy-aware data augmentation approach for LLM-based patient-trial matching (LLM-PTM), which balances the benefits of LLMs while ensuring the security …

Split Learning For Distributed Collaborative Training Of Deep Learning Models In Health Informatics, Zhuohang Li, Chao Yan, Xinmeng Zhang, Gharib Gharibi, Zhijun Yin, Xiaoqian Jiang, Bradley A Malin

Faculty, Staff and Student Publications

Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We …

Integrating Comorbidity Knowledge For Alzheimer's Disease Drug Repurposing Using Multi-Task Graph Neural Network, Ko-Hong Lin, Kang-Lin Hsieh, Xiaoqian Jiang, Yejin Kim

Faculty, Staff and Student Publications

Alzheimer's Disease (AD) is a multifactorial disease that shares common etiologies with its multiple comorbidities, especially vascular diseases. To predict repurposable drugs for AD utilizing the relatively well-investigated comorbidities' knowledge, we proposed a multi-task graph neural network (GNN)-based pipeline that incorporates the corresponding biomedical interactome of these diseases with their genetic markers and effective therapeutics. Our pipeline can accurately capture the interactions and disease classification in the network. Next, we predicted drugs that might interact with the AD module by the node embedding similarity. Our candidates are mostly BBB permeable, and literature evidence showed their potential for treating AD pathologies, …

Local Contrastive Learning For Medical Image Recognition, Syed A Rizvi, Ruixiang Tang, Xiaoqian Jiang, Xiaotian Ma, Xia Hu

Faculty, Staff and Student Publications

The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model predictions. In this work, we propose Local Region Contrastive Learning (LRCLR), a flexible fine-tuning framework that adds layers for significant image region selection as well as cross-modality …

Federated Learning Based Futuristic Biomedical Big-Data Analysis And Standardization, Afifa Salsabil Fathima, Syed Muzamil Basha, Syed Thouheed Ahmed, Sandeep Kumar Mathivanan, Sukumar Rajendran, Saurav Mallik, Zhongming Zhao

Faculty, Staff and Student Publications

Medical data processing and analytics exert significant influence in furnishing dependable decision support for prospective biomedical applications. Given the sensitive nature of medical data, specialized techniques and frameworks tailored for application-centric processing are imperative. This article presents a conceptualization for the analysis and uniformitarian of datasets through the implementation of Federated Learning (FL). The realm of medical big data stems from diverse origins, necessitating the delineation of data provenance and attribute paradigms to facilitate feature extraction and dependency assessment. The architecture governing the data collection framework is intricately linked to remote data transmission, thereby engendering efficient customization oversight. The operational …

การขจัดสีย้อมอินทรีย์ด้วยการดูดซับบนเมโซพอรัสซิลิกาโหลดด้วยโลหะ, อภิญญา ศิริพร

Chulalongkorn University Theses and Dissertations (Chula ETD)

ความนิยมที่มากขึ้นในการใช้งานสีย้อมอินทรีย์ในอุตสาหกรรมสิ่งทอ จึงทำให้เกิดความกังวลเป็นอย่างมากเนื่องจากสีย้อมอินทรีย์เป็นอันตรายและก่อให้เกิดมลพิษทางน้ำ ความเป็นพิษของสีย้อมเหล่านี้ส่งผลเสียต่อระบบนิเวศ สีย้อมเหล่านี้มีความเสถียรมากเนื่องจากสีย้อมเหล่านี้ได้จากการสังเคราะห์และมีโครงสร้างโมเลกุลที่ซับซ้อน ดังนั้นงานวิจัยนี้จึงศึกษาการกำจัดสีย้อมที่ปนเปื้อนในน้ำโดยการดูดซับด้วยตัวดูดซับเมโซพอรัสซิลิกาและตัวดูดซับเมโซพอรัสซิลิกาที่โหลดด้วยโลหะ ตัวดูดซับเมโซพอรัสซิลิกาจะเตรียมโดยใช้วิธีแม่แบบชนิดอ่อน สีย้อมอินทรีย์ที่เลือกใช้ ได้แก่ สีย้อมเอซิดบลูซึ่งเป็นสีย้อมที่มีประจุลบ และสีย้อมเมทิลีนบลูซึ่งเป็นสีย้อมที่มีประจุบวก โดยมีการศึกษาผลของปัจจัยต่าง ๆ ที่มีต่อการดูดซับซึ่งจะรายงานผลในเทอมของร้อยละการกำจัดสีย้อม ลักษณะเฉพาะของตัวดูดซับสังเคราะห์จะวิเคราะห์ด้วยด้วยเทคนิคการเลี้ยวเบนของรังสีเอกซ์ การดูดซับการคายซับด้วยก๊าซไนโตรเจน เทคนิคฟูเรียร์ทรานส์ฟอร์มอินฟราเรดสเปกโตรสโคปี และ ผลรวมของประจุบนพื้นผิวตัวดูดซับเท่ากับศูนย์ จากผลการทดลองพบว่า ตัวดูดซับ 15Zn/MS600nc สามารถกำจัดสีย้อมเอซิดบลูได้ร้อยละ 100 เมื่อใช้ความเข้มข้นเริ่มต้นของสีย้อมเอซิดบลู 300 มิลลิกรัมต่อลิตร ความเป็นกรดด่างเท่ากับ 7 และปริมาณตัวดูดซับ 0.1 กรัม ภายในเวลา 15 นาที ตัวดูดซับ 15Zn/MS600c แสดงร้อยละการกำจัดสีย้อมสีเมทิลีนบลูที่ร้อยละ 94 เมื่อใช้ความเข้มข้นเริ่มต้นของสีย้อมเมทิลีนบลู 100 มิลลิกรัมต่อลิตร ความเป็นกรดด่างเท่ากับ 11 และปริมาณตัวดูดซับ 0.1 กรัม ภายในเวลา 15 นาที จากการศึกษาแบบจำลองจลนพลศาสตร์การดูดซับ พบว่า การดูดซับบนตัวดูดซับ 15Zn/MS600nc มีความสอดคล้องกับแบบจำลองของอาฟ์รามี โดยมีค่าสัมประสิทธิ์สหสัมพัทธ์เท่ากับ 0.9995 ในขณะที่ตัวดูดซับ 15Zn/MS600c เมื่อพิจารณาจากค่าสัมประสิทธิ์สหสัมพัทธ์ซึ่งมีค่าเท่ากับ 0.9695 จะมีความสอดคล้องกับแบบจำลองปฏิกิริยาอันดับสองเทียม

Effects Of Salix Psammophila On Groundwater Recharge In A Semiarid Area Of North China, Lizhu Hou, Jingdong Gao, Bill X. Hu, Xixi Wang

Civil & Environmental Engineering Faculty Publications

Study region: The semiarid Mu Us Sandy Land (MUSL) was selected for this study. It is in the farming-pastoral ecotone of north China and functions as an eco-environmental barrier.

Study focus: Afforestation can mitigate desertification and soil erosion by improving hydrologic condition, which is particularly true for semiarid and arid regions. However, little is known about the quantitative response of hydrologic improvement to afforestation level that can be measured by leaf area index (LAI). The objective was to setup and use a physically-based model to quantitatively assess the dynamics of water fluxes from Salix psammophila afforestation in the MUSL.

New …

Eeg-Based Spanish Language Proficiency Classification: An Eeg Power Spectrum And Cross-Spectrum Analysis, Blaise Xavier O'Mara, Skyler Baumer

Honors Theses and Capstones

Second language proficiency may be predicted with electrophysiological techniques. In a machine learning application, this electrophysiological data may be used for language instructors and language students to assess their language learning. This study identifies how electroencephalogram (EEG) power spectrum and cross spectrum data of the brain cortex relates to Spanish second language (L2) proficiency of 20 Spanish language students of varying proficiency levels at the University of New Hampshire. The two metrics for assessing cortical power and processing were event-related desynchronization (ERD)—a measure of relative change in power—of the alpha (8-12 Hz) brain frequency band, and alpha and beta (13-30Hz) …

Occurrence Of Per- And Polyfluoroalkyl Substances (Pfas) In New Hampshire Biosolids, Katherine A. Wieck

Honors Theses and Capstones

Per- and polyfluoroalkyl substances (PFAS) are a group of over 4,000 compounds used in the manufacturing of products including aqueous film forming foams for firefighting, stain repellents, waterproofing agents, and nonstick cookware since their initial development in the 1940s. The long fluorinated carbon chain structure of PFAS causes chemical and thermal stability, and thus resistance to biodegradation. Biosolids produced at wastewater facilities for uses such as agricultural land-applied compost and fertilizer for lawns and athletic fields, as well as sludge disposed in landfills can cause contamination of groundwater and surface water. This poses a significant threat to human and environmental …

Determining Deuterium Polarization Via Nuclear Magnetic Resonance, Nicholas Muche

Honors Theses and Capstones

Dynamic Nuclear Polarization (DNP) is a method by which the spins of electrons are specifically targeted by microwaves at resonance frequency in order to induce spin flips. Occasionally, the electron spins couple with nuclear spins and nuclear spin flips are induced. The relaxation time of the electron is significantly lower than that of the nuclei, which allows for large polarization. The main goal of the Slifer Lab is to utilize specifically prepared crystalline structures in order to separate the convoluted energy transitions ("Batman Peak") in the deuterium's nuclear magnetic resonance (NMR) signal, which would allow for tensor enhancement of deuterium. …

Cup Is Essential For Oskar Mrna Translational Repression During Early Drosophila Oogenesis, Livia V. Bayer, Samantha Milano, Stephen K. Formel, Harpreet Kaur, Rishi Ravichandran, Juan A. Cambeiro, Lizaveta Slinko, Irina E. Catrina, Diana P. Bratu

Publications and Research

Study of the timing and location for mRNA translation across model systems has begun to shed light on molecular events fundamental to such processes as intercellular communication, morphogenesis, and body pattern formation. In D. melanogaster, the posterior mRNA determinant, oskar, is transcribed maternally but translated only when properly localized at the oocyte’s posterior cortex. Two effector proteins, Bruno1 and Cup, mediate steps of oskar mRNA regulation. The current model in the field identifies Bruno1 as necessary for Cup’s recruitment to oskar mRNA and indispensable for oskar’s translational repression. We now report that this Bruno1-Cup interaction leads …

Single Inclusive Hadron Production In Dis At Small X: Next To Leading Order Corrections, Filip Bergabo, Jamal Jalilian-Marian

Publications and Research

We calculate the one-loop corrections to single inclusive hadron production in Deep Inelastic Scattering (DIS) at small x in the forward rapidity region using the Color Glass Condensate formalism. We show that the divergent parts of the next to leading order (NLO) corrections either cancel among each other or lead to x (rapidity) evolution of the leading order (LO) dipole cross section according to the JIMWLK evolution equation and DGLAP evolution of the parton-hadron fragmentation function. The remaining finite parts constitute the NLO (α_s) corrections to the LO single inclusive hadron production cross section in DIS at …

Ecological Niche Comparison Among Closely Related Tree Species Of Lauraceae Using Climatic And Edaphic Data, Laura J. Giraldo-Kalil, Gonzalo E. Pinilla Buitrago, Andrés Lira-Noriega, Francisco Lorea-Hernández, Juan Núñez-Farfán

Publications and Research

ABSTRACT:

Edaphic specialization is considered to promote ecological differentiation among closely related species of Damburneya (Lauraceae) occurring in sympatry. However, little is known about the effects of soil and other key environmental factors like climate on the ecological niche and distribution of these tree species. Here, we assessed the role of climate and soil on niche divergence and potential distribution of four Damburneya species whose distributions span Central America and Mexico. We performed ecological niche modeling with MaxEnt using three sets of environmental data: climatic-only, edaphic-only, and a combination of both, to characterize species niches and suitable distribution areas. Niche …

Molecular Mechanism Behind The Safe Immunostimulatory Effect Of Withania Somnifera, Kriti Kalpana, Shen Yap, Moriya Tsuji, Akira Kawamura

Publications and Research

Withania somnifera (L.) Dunal (family Solanaceae) is a medicinal plant known for, among many pharmacological properties, an immune boosting effect. Our recent study revealed that its key immunostimulatory factor is lipopolysaccharide of plant-associated bacteria. This is peculiar, because, although LPS can elicit protective immunity, it is an extremely potent pro-inflammatory toxin (endotoxin). However, W. somnifera is not associated with such toxicity. In fact, despite the presence of LPS, it does not trigger massive inflammatory responses in macrophages. To gain insights into the safe immunostimulatory effect of W. somnifera, we conducted a mechanistic study on its major phytochemical constituent, …

Structure–Activity Relationship Studies On 6-Chloro-1-Phenylbenzazepines Leads To The Identification Of A New Dopamine D1 Receptor Antagonist, Rajan Giri, Hari K. Namballa, Vishwashiv Emogaje, Wayne W. Harding

Publications and Research

The 1-phenylbenzazepine template has yielded a number of D1R-like ligands, which, though useful as pharmacological tools, have significant drawbacks in terms of selectivity versus D5R as well as pharmacokinetic behavior. A number of 1-phenylbenzazepines contain a 6-chloro functional group, but extensive SAR studies around the 6-chloro-1-phenylbenzazepine framework have not been reported in the literature. To further understand the tolerance of the 6-chloro-1-phenylbenzazepine template for various substituent groups towards affinity and selectivity at D1R, we synthesized two series of analogs with structural variations at the C-7, C-8, N-3, C-3′ and C-4′ positions. The series 2 analogs differed from …

Changes To Sea Surface Temperatures And Vertical Wind Shear And Their Influence On Tropical Cyclone Activity In The Caribbean And The Main Developing Region, Keneshia Hibbert, Equisha Glenn, Thomas M. Smith, Jorge E. González-Cruz

Publications and Research

Sea surface temperatures and vertical wind shear are essential to tropical cyclone formation. TCs need warm SSTs and low shear for genesis. Increasing SSTs and decreasing VWS influences storm development. This work analyzes SST and VWS trends for the Caribbean, surrounding region, and the Atlantic hurricane main developing region from 1982–2020. Storm intensity increases significantly during this period. Annual and seasonal trends show that regional SSTs in the MDR are warming annually at 0.0219 °C yr⁻¹ and, per season, 0.0280 °C yr⁻¹. Simultaneously, VWS decreases during the late rainfall season, at 0.056 m/s yr⁻¹ in the …

Inclusive Random Sampling In Graphs And Networks, Yitzchak Novick, Amotz Bar-Noy

Publications and Research

It is often of interest to sample vertices from a graph with a bias towards higher-degree vertices. One well-known method, which we call random neighbor or RN, involves taking a vertex at random and exchanging it for one of its neighbors. Loosely inspired by the friendship paradox, the method is predicated on the fact that the expected degree of the neighbor is greater than or equal to the expected degree of the initial vertex. Another method that is actually perfectly analogous to the friendship paradox is random edge, or RE, where an edge is sampled at random, and then one …

Lead Induced Differences In Bone Properties In Osteocalcin +/+ And −/− Female Mice, G. Yildirim, W. C. Budell, O. Berezovska, S. Yagerman, S. S. Maliath, P. Mastrokostas, S. Tommasini, T. L. Dowd

Publications and Research

Lead (Pb) toxicity is a major health problem and bone is the major reservoir. Lead is detrimental to bone, affects bone remodelingand is associated with elderly fractures. Osteocalcin (OC) affects bone remodeling, improves fracture resistance and decreases with age and in some diseases. The effect of lead in osteocalcin depleted bone is unknown and of interest. We compared bone mineral properties of control and Pb exposed (from 2 to 6 months) femora from female adult C57BL6 OC+/+ and OC−/− mice using Fourier Transform Infrared Imaging (FTIRI), Micro-computed tomography (uCT), bone biomechanical measurements and serum turnover markers (P1NP, CTX). Lead significantly …