6,466 research outputs found
Machine learning in solar physics
The application of machine learning in solar physics has the potential to
greatly enhance our understanding of the complex processes that take place in
the atmosphere of the Sun. By using techniques such as deep learning, we are
now in the position to analyze large amounts of data from solar observations
and identify patterns and trends that may not have been apparent using
traditional methods. This can help us improve our understanding of explosive
events like solar flares, which can have a strong effect on the Earth
environment. Predicting hazardous events on Earth becomes crucial for our
technological society. Machine learning can also improve our understanding of
the inner workings of the sun itself by allowing us to go deeper into the data
and to propose more complex models to explain them. Additionally, the use of
machine learning can help to automate the analysis of solar data, reducing the
need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a
Living Review in Solar Physics (LRSP
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
Studies on genetic and epigenetic regulation of gene expression dynamics
The information required to build an organism is contained in its genome and the first
biochemical process that activates the genetic information stored in DNA is transcription.
Cell type specific gene expression shapes cellular functional diversity and dysregulation
of transcription is a central tenet of human disease. Therefore, understanding
transcriptional regulation is central to understanding biology in health and disease.
Transcription is a dynamic process, occurring in discrete bursts of activity that can be
characterized by two kinetic parameters; burst frequency describing how often genes
burst and burst size describing how many transcripts are generated in each burst. Genes
are under strict regulatory control by distinct sequences in the genome as well as
epigenetic modifications. To properly study how genetic and epigenetic factors affect
transcription, it needs to be treated as the dynamic cellular process it is. In this thesis, I
present the development of methods that allow identification of newly induced gene
expression over short timescales, as well as inference of kinetic parameters describing
how frequently genes burst and how many transcripts each burst give rise to. The work is
presented through four papers:
In paper I, I describe the development of a novel method for profiling newly transcribed
RNA molecules. We use this method to show that therapeutic compounds affecting
different epigenetic enzymes elicit distinct, compound specific responses mediated by
different sets of transcription factors already after one hour of treatment that can only
be detected when measuring newly transcribed RNA.
The goal of paper II is to determine how genetic variation shapes transcriptional bursting.
To this end, we infer transcriptome-wide burst kinetics parameters from genetically
distinct donors and find variation that selectively affects burst sizes and frequencies.
Paper III describes a method for inferring transcriptional kinetics transcriptome-wide
using single-cell RNA-sequencing. We use this method to describe how the regulation of
transcriptional bursting is encoded in the genome. Our findings show that gene specific
burst sizes are dependent on core promoter architecture and that enhancers affect burst
frequencies. Furthermore, cell type specific differential gene expression is regulated by
cell type specific burst frequencies.
Lastly, Paper IV shows how transcription shapes cell types. We collect data on cellular
morphologies, electrophysiological characteristics, and measure gene expression in the
same neurons collected from the mouse motor cortex. Our findings show that cells
belonging to the same, distinct transcriptomic families have distinct and non-overlapping
morpho-electric characteristics. Within families, there is continuous and correlated
variation in all modalities, challenging the notion of cell types as discrete entities
Recommended from our members
The Economics of Information and Communication Technologies in our Society
Information and Communication Technologies (ICTs) play a fundamental role in today\u27s society. As ICTs they become more mature and widely adopted, societies become more dependent on their use to operationalize daily activities. However, there are multiple societal impacts of ICTs that are not yet well understood. In this dissertation, I explore three different aspects of ICTs that have been widely discussed by media and industry during recent years. I analyze these topics from an economic perspective, contributing to the debate with rigorous modeling and the ensuing discussion of its implications. First, I study the impact that the COVID-19 pandemic had on remote meeting technologies\u27 usage. Second, I empirically tackle the long debated question of whether internet users perceive internet providers\u27 Network Neutrality practices. Finally, I analyze the most recent and ambitious public policy in the U.S. to improve households\u27 broadband internet connectivity - the so-called policy of bridging of the digital divide
Multimodal spatio-temporal deep learning framework for 3D object detection in instrumented vehicles
This thesis presents the utilization of multiple modalities, such as image and lidar, to incorporate spatio-temporal information from sequence data into deep learning architectures for 3Dobject detection in instrumented vehicles. The race to autonomy in instrumented vehicles or self-driving cars has stimulated significant research in developing autonomous driver assistance systems (ADAS) technologies related explicitly to perception systems. Object detection plays a crucial role in perception systems by providing spatial information to its subsequent modules; hence, accurate detection is a significant task supporting autonomous driving. The advent of deep learning in computer vision applications and the availability of multiple sensing modalities such as 360° imaging, lidar, and radar have led to state-of-the-art 2D and 3Dobject detection architectures. Most current state-of-the-art 3D object detection frameworks consider single-frame reference. However, these methods do not utilize temporal information associated with the objects or scenes from the sequence data. Thus, the present research hypothesizes that multimodal temporal information can contribute to bridging the gap between 2D and 3D metric space by improving the accuracy of deep learning frameworks for 3D object estimations. The thesis presents understanding multimodal data representations and selecting hyper-parameters using public datasets such as KITTI and nuScenes with Frustum-ConvNet as a baseline architecture. Secondly, an attention mechanism was employed along with convolutional-LSTM to extract spatial-temporal information from sequence data to improve 3D estimations and to aid the architecture in focusing on salient lidar point cloud features. Finally, various fusion strategies are applied to fuse the modalities and temporal information into the architecture to assess its efficacy on performance and computational complexity. Overall, this thesis has established the importance and utility of multimodal systems for refined 3D object detection and proposed a complex pipeline incorporating spatial, temporal and attention mechanisms to improve specific, and general class accuracy demonstrated on key autonomous driving data sets
Antimicrobial Peptides Aka Host Defense Peptides – From Basic Research to Therapy
This Special Issue reprint will address the most current and innovative developments in the field of HDP research across a range of topics, such as structure and function analysis, modes of action, anti-microbial effects, cell and animal model systems, the discovery of novel host-defense peptides, and drug development
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs
in a variety of areas, including computer vision, natural language
understanding, speech recognition, and reinforcement learning. Specialized,
high-performing neural architectures are crucial to the success of deep
learning in these areas. Neural architecture search (NAS), the process of
automating the design of neural architectures for a given task, is an
inevitable next step in automating machine learning and has already outpaced
the best human-designed architectures on many tasks. In the past few years,
research in NAS has been progressing rapidly, with over 1000 papers released
since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized
and comprehensive guide to neural architecture search. We give a taxonomy of
search spaces, algorithms, and speedup techniques, and we discuss resources
such as benchmarks, best practices, other surveys, and open-source libraries
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