227 research outputs found
Investigating ultrasound–light interaction in scattering media
Significance: Ultrasound-assisted optical imaging techniques, such as ultrasound-modulated optical tomography, allow for imaging deep inside scattering media. In these modalities, a fraction of the photons passing through the ultrasound beam is modulated. The efficiency by which the photons are converted is typically referred to as the ultrasound modulation’s “tagging efficiency.” Interestingly, this efficiency has been defined in varied and discrepant fashion throughout the scientific literature.
Aim: The aim of this study is the ultrasound tagging efficiency in a manner consistent with its definition and experimentally verify the contributive (or noncontributive) relationship between the mechanisms involved in the ultrasound optical modulation process.
Approach: We adopt a general description of the tagging efficiency as the fraction of photons traversing an ultrasound beam that is frequency shifted (inclusion of all frequency-shifted components). We then systematically studied the impact of ultrasound pressure and frequency on the tagging efficiency through a balanced detection measurement system that measured the power of each order of the ultrasound tagged light, as well as the power of the unmodulated light component.
Results: Through our experiments, we showed that the tagging efficiency can reach 70% in a scattering phantom with a scattering anisotropy of 0.9 and a scattering coefficient of 4 mm⁻¹ for a 1-MHz ultrasound with a relatively low (and biomedically acceptable) peak pressure of 0.47 MPa. Furthermore, we experimentally confirmed that the two ultrasound-induced light modulation mechanisms, particle displacement and refractive index change, act in opposition to each other.
Conclusion: Tagging efficiency was quantified via simulation and experiments. These findings reveal avenues of investigation that may help improve ultrasound-assisted optical imaging techniques
Investigating ultrasound–light interaction in scattering media
Significance: Ultrasound-assisted optical imaging techniques, such as ultrasound-modulated optical tomography, allow for imaging deep inside scattering media. In these modalities, a fraction of the photons passing through the ultrasound beam is modulated. The efficiency by which the photons are converted is typically referred to as the ultrasound modulation’s “tagging efficiency.” Interestingly, this efficiency has been defined in varied and discrepant fashion throughout the scientific literature.
Aim: The aim of this study is the ultrasound tagging efficiency in a manner consistent with its definition and experimentally verify the contributive (or noncontributive) relationship between the mechanisms involved in the ultrasound optical modulation process.
Approach: We adopt a general description of the tagging efficiency as the fraction of photons traversing an ultrasound beam that is frequency shifted (inclusion of all frequency-shifted components). We then systematically studied the impact of ultrasound pressure and frequency on the tagging efficiency through a balanced detection measurement system that measured the power of each order of the ultrasound tagged light, as well as the power of the unmodulated light component.
Results: Through our experiments, we showed that the tagging efficiency can reach 70% in a scattering phantom with a scattering anisotropy of 0.9 and a scattering coefficient of 4 mm⁻¹ for a 1-MHz ultrasound with a relatively low (and biomedically acceptable) peak pressure of 0.47 MPa. Furthermore, we experimentally confirmed that the two ultrasound-induced light modulation mechanisms, particle displacement and refractive index change, act in opposition to each other.
Conclusion: Tagging efficiency was quantified via simulation and experiments. These findings reveal avenues of investigation that may help improve ultrasound-assisted optical imaging techniques
Comparative transcriptomics analysis on Senecavirus A-infected and non-infected cells
Senecavirus A (SVA) is an emerging virus that causes the vesicular disease in pigs, clinically indistinguishable from other high consequence vesicular diseases. This virus belongs to the genus Senecavirus in the family Picornaviridae. Its genome is a positive-sense, single-stranded RNA, approximately 7,300 nt in length, with a 3′ poly(A) tail but without 5′-end capped structure. SVA can efficiently propagate in different cells, including some non-pig-derived cell lines. A wild-type SVA was previously rescued from its cDNA clone using reverse genetics in our laboratory. In the present study, the BSR-T7/5 cell line was inoculated with the passage-5 SVA. At 12 h post-inoculation, SVA-infected and non-infected cells were independently collected for the analysis on comparative transcriptomics. The results totally showed 628 differentially expressed genes, including 565 upregulated and 63 downregulated ones, suggesting that SVA infection significantly stimulated the transcription initiation in cells. GO and KEGG enrichment analyses demonstrated that SVA exerted multiple effects on immunity-related pathways in cells. Furthermore, the RNA sequencing data were subjected to other in-depth analyses, such as the single-nucleotide polymorphism, transcription factors, and protein–protein interactions. The present study, along with our previous proteomics and metabolomics researches, provides a multi-omics insight into the interaction between SVA and its hosts
Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes
PURPOSE: The medical literature relevant to germline genetics is growing
exponentially. Clinicians need tools monitoring and prioritizing the literature
to understand the clinical implications of the pathogenic genetic variants. We
developed and evaluated two machine learning models to classify abstracts as
relevant to the penetrance (risk of cancer for germline mutation carriers) or
prevalence of germline genetic mutations. METHODS: We conducted literature
searches in PubMed and retrieved paper titles and abstracts to create an
annotated dataset for training and evaluating the two machine learning
classification models. Our first model is a support vector machine (SVM) which
learns a linear decision rule based on the bag-of-ngrams representation of each
title and abstract. Our second model is a convolutional neural network (CNN)
which learns a complex nonlinear decision rule based on the raw title and
abstract. We evaluated the performance of the two models on the classification
of papers as relevant to penetrance or prevalence. RESULTS: For penetrance
classification, we annotated 3740 paper titles and abstracts and used 60% for
training the model, 20% for tuning the model, and 20% for evaluating the model.
The SVM model achieves 89.53% accuracy (percentage of papers that were
correctly classified) while the CNN model achieves 88.95 % accuracy. For
prevalence classification, we annotated 3753 paper titles and abstracts. The
SVM model achieves 89.14% accuracy while the CNN model achieves 89.13 %
accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts
as relevant to penetrance or prevalence. By facilitating literature review,
this tool could help clinicians and researchers keep abreast of the burgeoning
knowledge of gene-cancer associations and keep the knowledge bases for clinical
decision support tools up to date
MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation
Unsupervised domain adaption has been widely adopted in tasks with scarce
annotated data. Unfortunately, mapping the target-domain distribution to the
source-domain unconditionally may distort the essential structural information
of the target-domain data, leading to inferior performance. To address this
issue, we firstly propose to introduce active sample selection to assist domain
adaptation regarding the semantic segmentation task. By innovatively adopting
multiple anchors instead of a single centroid, both source and target domains
can be better characterized as multimodal distributions, in which way more
complementary and informative samples are selected from the target domain. With
only a little workload to manually annotate these active samples, the
distortion of the target-domain distribution can be effectively alleviated,
achieving a large performance gain. In addition, a powerful semi-supervised
domain adaptation strategy is proposed to alleviate the long-tail distribution
problem and further improve the segmentation performance. Extensive experiments
are conducted on public datasets, and the results demonstrate that the proposed
approach outperforms state-of-the-art methods by large margins and achieves
similar performance to the fully-supervised upperbound, i.e., 71.4% mIoU on
GTA5 and 71.8% mIoU on SYNTHIA. The effectiveness of each component is also
verified by thorough ablation studies.Comment: Accepted by TPAMI-IEEE Transactions on Pattern Analysis and Machine
Intelligence. arXiv admin note: substantial text overlap with
arXiv:2108.0801
Zero-shot Clinical Entity Recognition using ChatGPT
In this study, we investigated the potential of ChatGPT, a large language
model developed by OpenAI, for the clinical named entity recognition task
defined in the 2010 i2b2 challenge, in a zero-shot setting with two different
prompt strategies. We compared its performance with GPT-3 in a similar
zero-shot setting, as well as a fine-tuned BioClinicalBERT model using a set of
synthetic clinical notes from MTSamples. Our findings revealed that ChatGPT
outperformed GPT-3 in the zero-shot setting, with F1 scores of 0.418 (vs.0.250)
and 0.620 (vs. 0.480) for exact- and relaxed-matching, respectively. Moreover,
prompts affected ChatGPT's performance greatly, with relaxed-matching F1 scores
of 0.628 vs.0.541 for two different prompt strategies. Although ChatGPT's
performance was still lower than that of the supervised BioClinicalBERT model
(i.e., relaxed-matching F1 scores of 0.628 vs. 0.870), our study demonstrates
the great potential of ChatGPT for clinical NER tasks in a zero-shot setting,
which is much more appealing as it does not require any annotation.Comment: 7 pages, 5 tables, 1 figur
Targeted, homology-driven gene insertion in stem cells by ZFN-loaded 'all-in-one' lentiviral vectors
Biased integration remains a key challenge for gene therapy based on lentiviral vector technologies. Engineering of next-generation lentiviral vectors targeting safe genomic harbors for insertion is therefore of high relevance. In a previous paper (Cai et et, 2014a), we showed the use of integrase-defective lentiviral vectors (IDLVs) as carriers of complete gene repair kits consisting of zinc-finger nuclease (ZFN) proteins and repair sequences, allowing gene correction by homologous recombination (HR). Here, we follow this strategy to engineer ZEN-loaded IDLVs that insert transgenes by a homology-driven mechanism into safe loci. This insertion mechanism is driven by time-restricted exposure of treated cells to ZFNs. We show targeted gene integration in human stem cells, including CD34+ hematopoietic progenitors and induced pluripotent stem cells (iPSCs). Notably, targeted insertions are identified in 89% of transduced iPSCs. Our findings demonstrate the applicability of nuclease-loaded 'all-in-one' IDLVs for site-directed gene insertion in stem cell based gene therapies
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