STEM Fellowship Journal最新文献_第3页

Comparative analysis of NX-2127 and ibrutinib in treating B-cell malignancies NX-2127与依鲁替尼治疗b细胞恶性肿瘤的比较分析

STEM Fellowship Journal Pub Date : 2022-06-10 DOI: 10.17975/sfj-2022-008

Jeon Subin

引用次数: 0

Detection of inconsistency between shape and motion in realistic female and male animation 现实女性和男性动画中形状和运动不一致的检测

STEM Fellowship Journal Pub Date : 2022-03-31 DOI: 10.17975/sfj-2022-005

Joseph Russell, Sunia Saboor, Manmeet Makkar, Arpita Barua, A. Thaler

{"title":"Detection of inconsistency between shape and motion in realistic female and male animation","authors":"Joseph Russell, Sunia Saboor, Manmeet Makkar, Arpita Barua, A. Thaler","doi":"10.17975/sfj-2022-005","DOIUrl":"https://doi.org/10.17975/sfj-2022-005","url":null,"abstract":"Capturing the motion of a person and retargeting it to a virtual character with a different body shape is common practice in computer animation. This inconsistency between motion and shape often makes animations look unrealistic. It remains unclear which aspects of an animation affect how realistic it is perceived. Previous research has found detection of the inconsistency between motion and shape in biometric virtual characters to be at chance level for actions that involve object manipulation. Here, we test whether similar results are obtained for actions not involving objects and compare the detection of inconsistency in realistic female and male animation for actions with and without object manipulation. For creating our stimuli, we used the animations of five pairs of female and male performers with large differences in body weight from the bmlRUB database when throwing a ball, lifting a box, jumping, and walking. For each actor pair, we created inconsistent animations by combining the body shape from one actor with the motion from the other actor. For the consistent stimuli, the body shape and motion came from the same actor. In each trial of the experiment, participants observed one consistent and one inconsistent animation and selected which of the two they perceived to be inconsistent. Our results showed that for both female and male animations, participants’ detection rate was above chance for walking, and was at chance level for throwing. For lifting and jumping, the detection rate was at chance level for female animations and above chance level for male animations. Overall, detection rate was low which is promising news for realistic human animations but tended to be higher for male animations. Future research should investigate a broader range of actions to determine which are perceptually most affected by motion retargeting.","PeriodicalId":268438,"journal":{"name":"STEM Fellowship Journal","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125874294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging machine learning methods to predict COVID-19 vulnerability in U.S. counties based on socioeconomic factors 利用机器学习方法根据社会经济因素预测美国各县的COVID-19脆弱性

STEM Fellowship Journal Pub Date : 2022-03-31 DOI: 10.17975/sfj-2022-004

Katharine Emily Lee, Cynthia Denise Lo, William Ren Xu, Robert Ye, C. Tomkins-Lane

{"title":"Leveraging machine learning methods to predict COVID-19 vulnerability in U.S. counties based on socioeconomic factors","authors":"Katharine Emily Lee, Cynthia Denise Lo, William Ren Xu, Robert Ye, C. Tomkins-Lane","doi":"10.17975/sfj-2022-004","DOIUrl":"https://doi.org/10.17975/sfj-2022-004","url":null,"abstract":"As COVID-19 gained pandemic status, the number of confirmed cases in the US surpassed that of all other countries. Although the virus spread throughout the US, not all areas were affected equally. This retrospective study aims to explore these inequalities through pre-pandemic socioeconomic characteristics by attempting to create a predictive model for COVID-19 vulnerability at the county level. A total of 103 features of socioeconomic data for 2610 US counties (out of a total of 3007) were sourced from various online databases such as the US Census Bureau, the US Department of Agriculture, and the Association of American Medical Colleges. Additionally, to quantify each county’s COVID-19 vulnerability, we defined 3 custom measures: incidence, mortality, and case fatality. These measurements were calculated using case and death data taken 29 days after each county’s first case. Machine learning classification algorithms – including random forest, multi-layer perceptron neural network and XGBoost – were then used to predict the incidence, mortality, and case fatality of US counties. Through analysis, we were able to predict a county’s COVID-19 incidence with ~47% accuracy, mortality with ~59% accuracy, and case fatality with ~61% accuracy by looking solely at pre-pandemic socioeconomic factors. A list of important features was extracted using a built-in XGBoost function for each vulnerability measure (incidence, mortality, and case fatality). Many of these features are typically associated with pandemic spread (e.g., population density and medical infrastructure), while other features were unexpected (e.g., education) and warrant further studies to identify their role in disease propagation. Furthermore, the difficulties our model experienced support the notion that region-specific policies play an important role in successfully mitigating this crisis. The moderate success achieved in this study proves the feasibility of using classifiers as a pandemic preparedness evaluation tool.","PeriodicalId":268438,"journal":{"name":"STEM Fellowship Journal","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125883854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the disparity between public perception of COVID-19 diagnostic tests and scientific evidence: A quantitative systematic review and cross-sectional survey 探索公众对COVID-19诊断检测的看法与科学证据之间的差异:一项定量系统评价和横断面调查

STEM Fellowship Journal Pub Date : 2022-03-31 DOI: 10.17975/sfj-2022-006

Shrey Acharya, A. M. Schmidt, Yoohyun Park, Z. Patel, Gavin Yuen, Sergio Raez-Villanueva

{"title":"Exploring the disparity between public perception of COVID-19 diagnostic tests and scientific evidence: A quantitative systematic review and cross-sectional survey","authors":"Shrey Acharya, A. M. Schmidt, Yoohyun Park, Z. Patel, Gavin Yuen, Sergio Raez-Villanueva","doi":"10.17975/sfj-2022-006","DOIUrl":"https://doi.org/10.17975/sfj-2022-006","url":null,"abstract":"Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The rapid transmission of this disease has resulted in the COVID-19 pandemic. Due to global panic and misinformation, the public has questioned the validity of COVID-19 diagnostic tests in terms of their sensitivity. Our study compared the public’s familiarity to the sensitivity of quantitative reverse transcription polymerase chain reaction (RT-qPCR) and antigen tests in Canada to scientific data. A quantitative systematic review of 47 primary literature sources was conducted to determine the sensitivity of the RT-qPCR and antigen tests. Simultaneously, a survey with 105 participants was carried out to ascertain the public’s perception of these tests. The average reported sensitivity of the RT-qPCR test across the literature was 94.7%, significantly higher than that of the antigen test at 72.9% (p > 0.05). The public’s assumptions regarding the sensitivities of the RT-qPCR and antigen tests were determined to be 70-90% and 70-100%, respectively. In contrast to the findings from the quantitative systematic review, there was a significant, positive correlation (r ~ 0.5, p > 0.05) on the perceived sensitivity of the RT-qPCR versus the antigen tests for a given respondent. A negative/positive perception of one test was correlated with a negative/positive perception of the other test. Although the RT-qPCR test is reportedly more sensitive than the antigen test, the public’s perception of the sensitivity of one test is similar and correlated with the sensitivity of the other test. These results suggest the need to communicate information to the public transparently to instill trust in both tests.","PeriodicalId":268438,"journal":{"name":"STEM Fellowship Journal","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126413690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Breast implant associated lymphoma: Textured breast implants and their association with cancer 乳房植入物相关淋巴瘤:有纹理的乳房植入物及其与癌症的关系

STEM Fellowship Journal Pub Date : 2022-03-10 DOI: 10.17975/sfj-2022-002

Serena Lai, Manisha Rullay

引用次数: 0

The effect of ARBs and ACEIs on SARS-CoV-2: Exploring the controversy arb和acei对SARS-CoV-2的影响:探讨争议

STEM Fellowship Journal Pub Date : 2022-03-10 DOI: 10.17975/sfj-2022-003

Seleem Badawy, Benjamin Miller

引用次数: 0

Changing the sources and usage of energy for a better and sustainable future for all: Proceedings from the 2021-2022 High School Big Data Challenge 改变能源的来源和使用，为所有人创造更美好和可持续的未来:2021-2022年高中大数据挑战赛论文集

STEM Fellowship Journal Pub Date : 2022-03-02 DOI: 10.17975/sfj-2022-001

{"title":"Changing the sources and usage of energy for a better and sustainable future for all: Proceedings from the 2021-2022 High School Big Data Challenge","authors":"","doi":"10.17975/sfj-2022-001","DOIUrl":"https://doi.org/10.17975/sfj-2022-001","url":null,"abstract":"STEM Fellowship’s High School Big Data Challenge is an inquiry-driven experiential learning program that provides students an opportunity to learn and apply the fundamentals of data science – a crucial skill set for a young researcher in the digital age – through independent research projects. The COVID-19 pandemic disrupted high school education, at the same time creating a “fertile ground” for interdisciplinary, student-driven STEM education. This year, we invited students to explore issues of Affordable and Clean Energy at the Individual and Community Levels and to suggest their own evidence-based solutions, using Open Data and the principles of Open Science. Students explored many topics, ranging from Greenhouse Gas Emissions of School Buses to Legitimacy of Electric Vehicles to be the Greener Alternative We developed in-depth learning modules designed to bridge the gap between traditional high school courseware and digital reality and computational science. The students learnt a broad range of data analytics tools and programming languages which are useful for uncovering hidden patterns, trends in structured and unstructured data. Some of the tools the students learnt and used include Python, R, LaTeX, and machine learning. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge, and wish them the best for their future endeavours. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without patronage of CC UNESCO and generous support of our sponsors: RBC Future Launch, Let’s Talk Science, Digital Science, Infor, SCWST, CISCO Networking Academy, Canadian Science Publishing, and the University of Calgary Hunter Hub for Entrepreneurial Thinking. It has been a privilege for us to witness the analytical capabilities of the data-native generation of students first hand, and we are certain all entrants will continue to demonstrate excellence in their respective academic and professional careers.","PeriodicalId":268438,"journal":{"name":"STEM Fellowship Journal","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130220732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning transcriptomic model for prediction of pan-drug chemotherapeutic sensitivity 预测泛药物化疗敏感性的深度学习转录组学模型

STEM Fellowship Journal Pub Date : 2022-01-10 DOI: 10.17975/sfj-2021-013

Eddie Guo, Pouria Torabi, Daiva E. Nielsen, Matthew Pietrosanu

{"title":"Deep learning transcriptomic model for prediction of pan-drug chemotherapeutic sensitivity","authors":"Eddie Guo, Pouria Torabi, Daiva E. Nielsen, Matthew Pietrosanu","doi":"10.17975/sfj-2021-013","DOIUrl":"https://doi.org/10.17975/sfj-2021-013","url":null,"abstract":"The emergence of precision oncology approaches has begun to inform clinical decision-making in diagnostic, prognostic, and treatment contexts. High-throughput technology has enabled machine learning algorithms to use the molecular characteristics of tumors to generate personalized therapies. However, precision oncology studies have yet to develop a predictive biomarker incorporating pan-cancer gene expression profiles to stratify tumors into similar drug sensitivity profiles. Here we show that a neural network with ten hidden layers accurately classifies pancancer cell lines into two distinct chemotherapeutic response groups based on a pan-drug dataset with 89.0% accuracy (AUC = 0.904). Using unsupervised clustering algorithms, we found a cohort of cell line gene expression data from the Genomics of Drug Sensitivity in Cancer could be clustered into two response groups with significant differences in pan-drug chemotherapeutic sensitivity. After applying the Boruta feature selection algorithm to this dataset, a deep learning model was developed to predict chemotherapeutic response groups. The model’s high classification efficacy validates our hypothesis that cell lines with similar gene expression profiles present similar pan-drug chemotherapeutic sensitivity. This finding provides evidence for the potential use of similar combinatorial biomarkers to select potent candidate drugs that maximize therapeutic response and minimize the cytotoxic burden. Future investigations should aim to recursively subcluster cell lines within the response clusters defined in this study to provide a higher resolution of potential patient response to chemotherapeutics.","PeriodicalId":268438,"journal":{"name":"STEM Fellowship Journal","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121559296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Sex and age impact prevalence and symptoms of vasovagal syncope: Implications for accurate diagnosis 性别和年龄影响血管迷走神经性晕厥的患病率和症状:对准确诊断的意义

STEM Fellowship Journal Pub Date : 2021-12-21 DOI: 10.17975/sfj-2021-008

Hailey Gregson, Ana Ivkov

引用次数: 0

Gaining the upper hand on COVID-19 misinformation 在COVID-19错误信息方面占上风

STEM Fellowship Journal Pub Date : 2021-12-21 DOI: 10.17975/sfj-2021-009

Alex Cen, Lara Parlatan

引用次数: 0