4,809 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport
Recently, Neural Topic Models (NTM), inspired by variational autoencoders,
have attracted a lot of research interest; however, these methods have limited
applications in the real world due to the challenge of incorporating human
knowledge. This work presents a semi-supervised neural topic modeling method,
vONTSS, which uses von Mises-Fisher (vMF) based variational autoencoders and
optimal transport. When a few keywords per topic are provided, vONTSS in the
semi-supervised setting generates potential topics and optimizes topic-keyword
quality and topic classification. Experiments show that vONTSS outperforms
existing semi-supervised topic modeling methods in classification accuracy and
diversity. vONTSS also supports unsupervised topic modeling. Quantitative and
qualitative experiments show that vONTSS in the unsupervised setting
outperforms recent NTMs on multiple aspects: vONTSS discovers highly clustered
and coherent topics on benchmark datasets. It is also much faster than the
state-of-the-art weakly supervised text classification method while achieving
similar classification performance. We further prove the equivalence of optimal
transport loss and cross-entropy loss at the global minimum.Comment: 24 pages, 12 figures, ACL findings 202
TeamSTEPPS and Organizational Culture
Patient safety issues remain despite several strategies developed for their deterrence. While many safety initiatives bring about improvement, they are repeatedly unsustainable and short-lived. The index hospital’s goal was to build an organizational culture within a groundwork that improves teamwork and continuing healthcare team engagement. Teamwork influences the efficiency of patient care, patient safety, and clinical outcomes, as it has been identified as an approach for enhancing collaboration, decreasing medical errors, and building a culture of safety in healthcare. The facility implemented Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS), an evidence-based framework which was used for team training to produce valuable and needed changes, facilitating modification of organizational culture, increasing patient safety compliance, or solving particular issues. This study aimed to identify the correlation between TeamSTEPPS enactment and improved organizational culture in the ambulatory care nursing department of a New York City public hospital
Generalizations for Cell Biological Explanations: Distinguishing between Principles and Laws
Laws have figured in the development of modern biology (e.g. Mendelian laws of inheritance), but there is a tacit assumption particularly in contemporary cell and molecular biology that laws are only of the 'strict' kind (e.g. the laws of motion or universal gravitation), which cell biology appears to lack. Moreover, the cell-biology-specific non-universal laws that do exist (e.g. scaling laws in biochemical networks within single cells) are few and far between. As discussed elsewhere (and not further argued for in this paper), mechanistic explanations, which are the dominant kind of explanation in cell biology, face significant challenges and their utility has been checkered in different biomedical areas. Just as laws and mechanisms figure in explanations in organic chemistry and ecology, fields that deal with lower- and higher-scale phenomena compared to cell biology, respectively, it should not be assumed that cell biology is somehow in a unique position where few or no laws could be discovered and used in its explanations. An impediment to discovering lawlike generalizations in cell biology is that the understanding of many cellular phenomena is still quite qualitative and imprecise. This paper is motivated by the premise that mechanisms and laws can both be in the foreground of explanations in cell biology and that a framework should be developed to encourage and facilitate the discovery of laws specific to and operative at the individual cell level. To that end, in the domain of scientifically-relevant non-universal (i.e. non-exceptionless) generalizations, which some philosophers equate with the notion of ceteris paribus laws (henceforth, 'cp-laws'), I propose that a cp-law might have one or more corresponding 'principles'. Using a running example of generalizations of oscillatory movements from physics with direct relevance to cell biology, I argue that while a cp-law and its paired principle(s) might have the same explanatory theme (e.g. explain the same phenomenon), a principle is broader in scope compared to its paired cp-law but less expectable or reliable in its predictions. This is because principles appear to be more qualitative and less numerically precise compared to cp-laws, reflective of our lack of precise understanding of the systems to which the generalizations apply. The principles–laws concept makes for a more lenient approach for what could count as a lawlike generalization and can encourage the discovery of novel generalizations in areas of cell biology where no specific generalizations typically figure in explanations. A principle could be thought of as providing a program for explanation, whereas its paired law provides explanations for specific instances. Newly posited principles could augment mechanistic explanations and also potentially lead to the discovery of corresponding cp-laws
Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review
Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, as well as automation employed to amplify human efforts. Recent developments in machine learning models, such as Transformers, allow for training upon very large and sophisticated multimodal datasets and enable generalization across domains of knowledge. These models also herald an increasing emphasis on prompt engineering to provide qualitative fine-tuning upon the model itself, adding a novel emerging layer of direct machine learning annotation. These capabilities enable machine intelligence to recognize, predict, and emulate human behavior with much greater accuracy and nuance, a noted shortfall of which have contributed to algorithmic injustice in previous techniques. However, the scale and complexity of training data required for multimodal models presents engineering challenges. Best practices for conducting annotation for large multimodal models in the most safe and ethical, yet efficient, manner have not been established. This paper presents a systematic literature review of crowd and machine learning augmented behavioral annotation methods to distill practices that may have value in multimodal implementations, cross-correlated across disciplines. Research questions were defined to provide an overview of the evolution of augmented behavioral annotation tools in the past, in relation to the present state of the art. (Contains five figures and four tables)
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Noninvasive, low-cost RNA-sequencing enhances discovery potential of transcriptome studies
Transcriptome studies disentangle functional mechanisms of gene expression regulation and may lend key insights into disease mechanisms. However, the cost of RNA-sequencing and types of tissues currently assayed pose major limitations to study expansion and disease-relevant discovery. This thesis develops methods for sampling noninvasive biospecimens for transcriptome studies, investigating their technical and biological characteristics, and assessing the feasibility of using noninvasive samples in transcriptomic and clinical applications.
Chapter 1 explores the technical and biological features of four potential noninvasive sample types (buccal swabs, hair follicles, saliva, and urine cell pellets) in a pilot study of 19 individuals whereby four separate collections of each tissue were performed (i.e. 76 samples/tissue, 304 samples in total). From this data, consistency of library preparation, cell type content, replication of GTEx cis-eQTLs, and disease applications were assessed. In all, hair follicles and urine cell pellets were found to be most promising for future applications.
Chapter 2 investigates the scaling potential of noninvasive sampling in SPIROMICS, a COPD clinical cohort. To do so, 140 hair follicle and 110 buccal swab samples were collected from seven different clinical sites. Consistency of sample quality was observed to be high for hair follicles, and hair cell type abundance estimates were consistent within SPIROMICS and compared to the 19 subject pilot study. Mapping of cis-eQTLs in hair revealed 339 associations not identified in any prior study. These cis-eQTLs show higher replication in GTEx tissues that share cell types with hair follicles, indicating hair follicles may indeed capture gene expression regulatory mechanisms found in more invasive tissue types of the body.
This thesis suggests future use of noninvasive sampling will facilitate discovery by increasing sample sizes in more diverse populations and in tissues with greater cell type diversity and biological relatedness to disease mechanisms. Moreover, the nature of noninvasive sampling enables complex, longitudinal study designs with greater ability to capture context-dependent mechanisms of genetic regulation not currently able to be interrogated
An intersectoral approach to enhance surveillance and guide rabies control and elimination programs
Rabies is a viral zoonotic disease causing horrific neurological symptoms inevitably leading to death without prompt administration of post-exposure prophylaxis (PEP) to prevent infection. While any mammal can be infected by and transmit rabies, almost 99% of the estimated 59,000 human deaths per year are due to bites from rabid dogs, with the vast majority occurring in Asia and Africa. Through mass dog vaccinations starting in the 1920s, rabies has been successfully eliminated from domestic dog populations in practically all high-income countries. Yet, many lowand middle-income countries (LMICs) are still endemic and face extensive challenges controlling rabies and achieving elimination.
Strengthening surveillance through integrated intersectoral methods has been an important component of the Zero by 30 global strategy to eliminate human deaths from dog-mediated rabies by 2030. Similar to other neglected tropical diseases, only a small percentage of human and animal rabies cases are captured in surveillance at the local level, then go on to be reported in official national and international statistics. This lack of detection and underreporting has resulted in suboptimal data quality that conceals the true magnitude of the burden of rabies, leading to a cycle of neglect by reducing advocacy, funding, and stakeholder engagement. Hence, surveillance must be sufficiently enhanced to monitor, evaluate, and inform rabies control efforts to support LMICs to achieve elimination.
This thesis aims to critically review and evaluate the One Health approach, Integrated Bite Case Management (IBCM), as a cost-effective method to enhance rabies surveillance and guide control and elimination programs in LMICs, with a particular focus on a case study of IBCM implementation in the Philippines. The thesis is presented in a series of five chapters, starting with a general introduction (Chapter 1), followed by three standalone data chapters (Chapters 2, 3 & 4), and concluding with a general discussion (Chapter 5).
IBCM is a current gold standard surveillance method advocated by WHO and other international organizations. Yet, as a relatively novel One Health approach, there is still little understood about the implementation of IBCM in practice. In Chapter 2, I examined how IBCM is conceptualized by experts in the field, exploring variation in its operationalization in different epidemiological and geographical contexts.
Findings from this study highlighted the diversity of how IBCM can be organized within existing government systems/sectors and demonstrated it is not a one-sizefits-all approach. Contextual features of each location influenced the success of delivery and the potential impact of IBCM, with the issue of sustainability identified as one of the greatest challenges. For successful development and implementation of IBCM programs, this study recommends that more guidance is provided for health workers receiving bite patients on assessing rabies-risk, and for stakeholders and practitioners on how to tailor IBCM to fit the local context.
In Chapter 3, I explored this topic in more depth through the evaluation of a threeyear (January 2020 to December 2022) implementation study of IBCM in Oriental Mindoro province, Philippines. Using a mixed methods process evaluation, I assessed the feasibility and fidelity of effective delivery of IBCM, and how protocols were adapted to the context over the course of the study. The evaluation showed that the initial protocols envisioning trained government staff would uptake IBCM activities were not feasible due to implementation barriers. However, following adaptations made by the project team and participants, including adjustments for the COVID-19 pandemic, IBCM was delivering more effectively in 2021 and 2022. The findings concluded that, if implemented effectively, IBCM showed great promise as a strategy to enhance rabies surveillance in the Philippines, with evidence from the study providing key lessons for the adaptation and scale-up of IBCM to additional provinces in the Philippines.
In Chapter 4, I used data collected from enhanced IBCM surveillance in Oriental Mindoro province from the implementation study discussed in Chapter 3. This quantitative data was used to develop an adapted probabilistic decision tree model used to estimate the burden of rabies, evaluate surveillance performance, and assess the impact of current rabies prevention practices. Results from this study estimated a high incidence of bite patient presentations to health facilities (1,160/100,000 persons/year), with <2% deemed high-risk for rabies exposures (<25/100,000 persons/year) and an average of 71.4% of probable rabies-exposed patients seeking PEP. Routine surveillance confirmed <1% of circulating animal cases, whereas IBCM resulted in a fivefold increase in detection. The model estimated that between 275 to 838 dogs developed rabies; 18 to 28 deaths were averted by PEP; and total costs of over 16,730-38,240 USD per death averted, in Oriental Mindoro province. These findings highlight that current PEP practices in the Philippines are not cost-effective without concurrent strengthened risk-based surveillance to reduce the indiscriminate use of PEP. The study concludes that integrating IBCM into national policy has the potential to guide PEP administration to reduce unnecessary expenditure on PEP and inform rabies control measures.
Overall, this thesis exemplifies the value of enhancing rabies surveillance using a One Health approach to achieve Zero by 30 rabies elimination goals. Yet, the development and implementation of IBCM must be carefully considered and planned to ensure the effective delivery of IBCM activities leading to desired outputs and outcomes. With more guidance provided by international organizations to streamline protocols and procedures, the IBCM approach has the potential to be a key component of national strategies to monitor and evaluate the progress of rabies control efforts, verify elimination, and promptly detect incursion events
Intelligent computing : the latest advances, challenges and future
Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing
Towards Mobility Data Science (Vision Paper)
Mobility data captures the locations of moving objects such as humans,
animals, and cars. With the availability of GPS-equipped mobile devices and
other inexpensive location-tracking technologies, mobility data is collected
ubiquitously. In recent years, the use of mobility data has demonstrated
significant impact in various domains including traffic management, urban
planning, and health sciences. In this paper, we present the emerging domain of
mobility data science. Towards a unified approach to mobility data science, we
envision a pipeline having the following components: mobility data collection,
cleaning, analysis, management, and privacy. For each of these components, we
explain how mobility data science differs from general data science, we survey
the current state of the art and describe open challenges for the research
community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from
the metadata. PDF has not been change
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