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Article: Problem Formulation for Off-Target Effects of Externally Applied Double-Stranded RNA-Based Products for Pest Control.

Raybould, Alan / Burns, Andrea

2020 Volume 11, Page(s) 424

Abstract: Externally applied dsRNA-based biocontrol products may lead to off-target degradation of messenger RNA in target and non-target organisms. For the purposes of regulatory risk assessment of such products, producing a comprehensive catalog of any off- ... ...

Abstract	Externally applied dsRNA-based biocontrol products may lead to off-target degradation of messenger RNA in target and non-target organisms. For the purposes of regulatory risk assessment of such products, producing a comprehensive catalog of any off-target effects using profiling methods is unnecessary and would be ineffective in supporting decision-making. Instead, problem formulation should derive criteria that indicate acceptable risk and devise a plan to test the hypothesis that the product meets those criteria. The key to effective risk assessment of dsRNA-based biocontrols is determining whether their properties indicate acceptable or unacceptable risk, not whether they arise from on- or off-target effects of dsRNA.
Language	English
Publishing date	2020-04-16
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2711035-7
ISSN	1664-462X
ISSN	1664-462X
DOI	10.3389/fpls.2020.00424
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Topical Miconazole Cream and Warfarin Interaction: A Case Report.

Naberhaus, Taylor / Jones, Maura J / Burns, Andrea / Raney, Erin C

The Journal of pharmacy technology : jPT : official publication of the Association of Pharmacy Technicians

2022 Volume 38, Issue 2, Page(s) 127–129

Language	English
Publishing date	2022-01-22
Publishing country	United States
Document type	Journal Article
ZDB-ID	54478-4
ISSN	8755-1225
ISSN	8755-1225
DOI	10.1177/87551225211069490
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Article: Advancing ecological risk assessment on genetically engineered breeding stacks with combined insect-resistance traits

McDonald, Justin / Burns, Andrea / Raybould, Alan

Transgenic research. 2020 Feb., v. 29, no. 1

2020

Abstract: To inform the ecological risk assessment (ERA) of a transgenic crop with multiple insecticidal traits combined by conventional breeding (breeding stack), a comparative field study is customarily conducted to compare transgenic protein concentrations in a ...

Abstract	To inform the ecological risk assessment (ERA) of a transgenic crop with multiple insecticidal traits combined by conventional breeding (breeding stack), a comparative field study is customarily conducted to compare transgenic protein concentrations in a breeding stack to those in corresponding component single events used in the breeding process. This study tests the hypothesis that transgenic protein expression will not significantly increase due to stacking, such that existing margins of exposure erode to unacceptable levels. Corroboration of this hypothesis allows for the use of existing non-target organism (NTO) effects tests results, where doses were based on the estimated environmental concentrations determined for a component single event. Results from over 20 studies comparing expression profiles of insecticidal proteins produced by commercial events in various combinations of conventionally-bred stacks were examined to evaluate applying previously determined no-observed-effect concentrations (NOECs) to stack ERAs. This paper presents a large number of tests corroborating the hypothesis of no significant increase in insecticidal protein expression due to combination by conventional breeding, and much of the variation in protein expression is likely attributed to genetic and environmental factors. All transgenic protein concentrations were well within conservative margins between exposure and corresponding NOEC. This work supports the conclusion that protein expression data generated for single events and the conservative manner for setting NTO effects test concentrations allows for the transportability of existing NOECs to the ERA of conventionally-bred stacks, and that future tests of the stated hypothesis are no longer critically informative for ERA on breeding stacks.
Keywords	environmental assessment ; genetic engineering ; insecticidal proteins ; nontarget organisms ; protein synthesis ; transgenic plants
Language	English
Dates of publication	2020-02
Size	p. 135-148.
Publishing place	Springer International Publishing
Document type	Article
ZDB-ID	31620-9
ISSN	1573-9368 ; 0962-8819
ISSN (online)	1573-9368
ISSN	0962-8819
DOI	10.1007/s11248-019-00185-8
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Advancing ecological risk assessment on genetically engineered breeding stacks with combined insect-resistance traits.

McDonald, Justin / Burns, Andrea / Raybould, Alan

Transgenic research

2020 Volume 29, Issue 1, Page(s) 135–148

Abstract	To inform the ecological risk assessment (ERA) of a transgenic crop with multiple insecticidal traits combined by conventional breeding (breeding stack), a comparative field study is customarily conducted to compare transgenic protein concentrations in a breeding stack to those in corresponding component single events used in the breeding process. This study tests the hypothesis that transgenic protein expression will not significantly increase due to stacking, such that existing margins of exposure erode to unacceptable levels. Corroboration of this hypothesis allows for the use of existing non-target organism (NTO) effects tests results, where doses were based on the estimated environmental concentrations determined for a component single event. Results from over 20 studies comparing expression profiles of insecticidal proteins produced by commercial events in various combinations of conventionally-bred stacks were examined to evaluate applying previously determined no-observed-effect concentrations (NOECs) to stack ERAs. This paper presents a large number of tests corroborating the hypothesis of no significant increase in insecticidal protein expression due to combination by conventional breeding, and much of the variation in protein expression is likely attributed to genetic and environmental factors. All transgenic protein concentrations were well within conservative margins between exposure and corresponding NOEC. This work supports the conclusion that protein expression data generated for single events and the conservative manner for setting NTO effects test concentrations allows for the transportability of existing NOECs to the ERA of conventionally-bred stacks, and that future tests of the stated hypothesis are no longer critically informative for ERA on breeding stacks.
MeSH term(s)	Animals ; Crops, Agricultural/genetics ; Crops, Agricultural/parasitology ; Ecology ; Insecta/growth & development ; Insecticide Resistance/genetics ; Phenotype ; Plant Breeding ; Plants, Genetically Modified/genetics ; Plants, Genetically Modified/parasitology ; Risk Assessment/methods ; Transgenes
Language	English
Publishing date	2020-01-17
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	31620-9
ISSN	1573-9368 ; 0962-8819
ISSN (online)	1573-9368
ISSN	0962-8819
DOI	10.1007/s11248-019-00185-8
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Book ; Online: Unsupervised Disentanglement without Autoencoding

Burns, Andrea / Sarna, Aaron / Krishnan, Dilip / Maschinot, Aaron

Pitfalls and Future Directions

2021

Abstract: Disentangled visual representations have largely been studied with generative models such as Variational AutoEncoders (VAEs). While prior work has focused on generative methods for disentangled representation learning, these approaches do not scale to ... ...

Abstract	Disentangled visual representations have largely been studied with generative models such as Variational AutoEncoders (VAEs). While prior work has focused on generative methods for disentangled representation learning, these approaches do not scale to large datasets due to current limitations of generative models. Instead, we explore regularization methods with contrastive learning, which could result in disentangled representations that are powerful enough for large scale datasets and downstream applications. However, we find that unsupervised disentanglement is difficult to achieve due to optimization and initialization sensitivity, with trade-offs in task performance. We evaluate disentanglement with downstream tasks, analyze the benefits and disadvantages of each regularization used, and discuss future directions. Comment: Accepted at the ICML 2021 Self-Supervised Learning for Reasoning and Perception Workshop
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2021-08-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A case report of self-medication with over-the-counter fish antibiotic: Implications for pharmacists.

Burns, Andrea / Goodlet, Kellie J / Chapman, Alice / Roberts, Eugenia Popescu

Journal of the American Pharmacists Association : JAPhA

2020 Volume 60, Issue 4, Page(s) e121–e123

Abstract: Objective: The human use of over-the-counter antibiotics intended for the treatment of pet animals has been recognized as a potential barrier to antibiotic stewardship efforts. The objective of this report is to describe a case of self-medication with a ...

Abstract	Objective: The human use of over-the-counter antibiotics intended for the treatment of pet animals has been recognized as a potential barrier to antibiotic stewardship efforts. The objective of this report is to describe a case of self-medication with a fish antibiotic resulting in delayed medical treatment and provide recommendations for pharmacists practicing in outpatient settings on how to best identify and manage nonprescription antibiotic use. Case summary: A 24-year-old man experienced dental pain and "flu-like" symptoms for which he attempted self-treatment with oral amoxicillin 250 mg daily purchased by a family member from a pet store. The amoxicillin was marketed for the treatment of bacterial infection in pet fish. After several days of increasing tooth pain despite the self-medication, the patient presented to an outpatient clinic where he was found to have a molar abscess requiring tooth extraction. The patient responded well to therapy and was counseled to discontinue antibiotic self-treatment. Practice implications: Undocumented use of nonprescription antibiotics represents a threat to patient safety. Potential deleterious outcomes include resistance, adverse drug events, and delays in definitive infection treatment. Pharmacists should screen patients for nonprescription antibiotic use, provide them counseling on appropriate antibiotic use, and educate other health care professionals on underrecognized sources of nonprescription antibiotics to increase awareness of this growing issue. Furthermore, antibiotic resistance should be considered when recommending an antibiotic agent for the treatment of infections.
MeSH term(s)	Amoxicillin ; Anti-Bacterial Agents/administration & dosage ; Antimicrobial Stewardship ; Drug Misuse ; Humans ; Male ; Nonprescription Drugs ; Pharmacists ; Self Medication ; Veterinary Drugs/administration & dosage ; Young Adult
Chemical Substances	Anti-Bacterial Agents ; Nonprescription Drugs ; Veterinary Drugs ; Amoxicillin (804826J2HU)
Language	English
Publishing date	2020-01-30
Publishing country	United States
Document type	Case Reports ; Journal Article
ZDB-ID	2118585-2
ISSN	1544-3450 ; 1544-3191 ; 1086-5802
ISSN (online)	1544-3450
ISSN	1544-3191 ; 1086-5802
DOI	10.1016/j.japh.2019.12.020
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Book ; Online: A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibility

Burns, Andrea / Arsan, Deniz / Agrawal, Sanjna / Kumar, Ranjitha / Saenko, Kate / Plummer, Bryan A.

2022

Abstract: Vision-language navigation (VLN), in which an agent follows language instruction in a visual environment, has been studied under the premise that the input command is fully feasible in the environment. Yet in practice, a request may not be possible due ... ...

Abstract	Vision-language navigation (VLN), in which an agent follows language instruction in a visual environment, has been studied under the premise that the input command is fully feasible in the environment. Yet in practice, a request may not be possible due to language ambiguity or environment changes. To study VLN with unknown command feasibility, we introduce a new dataset Mobile app Tasks with Iterative Feedback (MoTIF), where the goal is to complete a natural language command in a mobile app. Mobile apps provide a scalable domain to study real downstream uses of VLN methods. Moreover, mobile app commands provide instruction for interactive navigation, as they result in action sequences with state changes via clicking, typing, or swiping. MoTIF is the first to include feasibility annotations, containing both binary feasibility labels and fine-grained labels for why tasks are unsatisfiable. We further collect follow-up questions for ambiguous queries to enable research on task uncertainty resolution. Equipped with our dataset, we propose the new problem of feasibility prediction, in which a natural language instruction and multimodal app environment are used to predict command feasibility. MoTIF provides a more realistic app dataset as it contains many diverse environments, high-level goals, and longer action sequences than prior work. We evaluate interactive VLN methods using MoTIF, quantify the generalization ability of current approaches to new app environments, and measure the effect of task feasibility on navigation performance. Comment: Accepted at the European Conference on Computer Vision (ECCV) 2022. This is a new version of the paper with additional experimental results and a few prior implementation bugs fixed
Keywords	Computer Science - Computation and Language ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Human-Computer Interaction
Subject code	004
Publishing date	2022-02-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: WikiWeb2M

Burns, Andrea / Srinivasan, Krishna / Ainslie, Joshua / Brown, Geoff / Plummer, Bryan A. / Saenko, Kate / Ni, Jianmo / Guo, Mandy

A Page-Level Multimodal Wikipedia Dataset

2023

Abstract: Webpages have been a rich resource for language and vision-language tasks. Yet only pieces of webpages are kept: image-caption pairs, long text articles, or raw HTML, never all in one place. Webpage tasks have resultingly received little attention and ... ...

Abstract	Webpages have been a rich resource for language and vision-language tasks. Yet only pieces of webpages are kept: image-caption pairs, long text articles, or raw HTML, never all in one place. Webpage tasks have resultingly received little attention and structured image-text data underused. To study multimodal webpage understanding, we introduce the Wikipedia Webpage 2M (WikiWeb2M) suite; the first to retain the full set of images, text, and structure data available in a page. WikiWeb2M can be used for tasks like page description generation, section summarization, and contextual image captioning. Comment: Accepted at the WikiWorkshop 2023. Data is readily available at https://github.com/google-research-datasets/wit/blob/main/wikiweb2m.md. arXiv admin note: text overlap with arXiv:2305.03668
Keywords	Computer Science - Computation and Language ; Computer Science - Computer Vision and Pattern Recognition
Publishing date	2023-05-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: A Suite of Generative Tasks for Multi-Level Multimodal Webpage Understanding

Burns, Andrea / Srinivasan, Krishna / Ainslie, Joshua / Brown, Geoff / Plummer, Bryan A. / Saenko, Kate / Ni, Jianmo / Guo, Mandy

2023

Abstract: Webpages have been a rich, scalable resource for vision-language and language only tasks. Yet only pieces of webpages are kept: image-caption pairs, long text articles, or raw HTML, never all in one place. Webpage tasks have resultingly received little ... ...

Abstract	Webpages have been a rich, scalable resource for vision-language and language only tasks. Yet only pieces of webpages are kept: image-caption pairs, long text articles, or raw HTML, never all in one place. Webpage tasks have resultingly received little attention and structured image-text data left underused. To study multimodal webpage understanding, we introduce the Wikipedia Webpage suite (WikiWeb2M) of 2M pages. We verify its utility on three generative tasks: page description generation, section summarization, and contextual image captioning. We design a novel attention mechanism Prefix Global, which selects the most relevant image and text content as global tokens to attend to the rest of the webpage for context. By using page structure to separate such tokens, it performs better than full attention with lower computational complexity. Experiments show that the new annotations from WikiWeb2M improve task performance compared to data from prior work. We also include ablations on sequence length, input features, and model size. Comment: Data can be downloaded at https://github.com/google-research-datasets/wit/blob/main/wikiweb2m.md
Keywords	Computer Science - Computation and Language ; Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2023-05-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Language-Guided Audio-Visual Source Separation via Trimodal Consistency

Tan, Reuben / Ray, Arijit / Burns, Andrea / Plummer, Bryan A. / Salamon, Justin / Nieto, Oriol / Russell, Bryan / Saenko, Kate

2023

Abstract: We propose a self-supervised approach for learning to perform audio source separation in videos based on natural language queries, using only unlabeled video and audio pairs as training data. A key challenge in this task is learning to associate the ... ...

Abstract	We propose a self-supervised approach for learning to perform audio source separation in videos based on natural language queries, using only unlabeled video and audio pairs as training data. A key challenge in this task is learning to associate the linguistic description of a sound-emitting object to its visual features and the corresponding components of the audio waveform, all without access to annotations during training. To overcome this challenge, we adapt off-the-shelf vision-language foundation models to provide pseudo-target supervision via two novel loss functions and encourage a stronger alignment between the audio, visual and natural language modalities. During inference, our approach can separate sounds given text, video and audio input, or given text and audio input alone. We demonstrate the effectiveness of our self-supervised approach on three audio-visual separation datasets, including MUSIC, SOLOS and AudioSet, where we outperform state-of-the-art strongly supervised approaches despite not using object detectors or text labels during training. Comment: Accepted at CVPR 2023
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence ; Computer Science - Computation and Language
Subject code	420
Publishing date	2023-03-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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