| 1. |
- Andrews, B. J., et al.
(författare)
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Quantitative human cell encyclopedia
- 2016
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Ingår i: Science Signaling. - : American Association for the Advancement of Science (AAAS). - 1945-0877 .- 1937-9145. ; 9:443
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Tidskriftsartikel (refereegranskat)abstract
- Scientists gathered to discuss the necessity, feasibility, and challenges of generating a quantitative catalog of the components in human cells that is essential for our understanding of human physiology in health and disease and to support future breakthroughs in treating diseases. This report summarizes the discussion that emerged at the Human Quantitative Dynamics Workshop held in Bethesda, MD, USA, in December 2015.
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| 2. |
- Matos-Maraví, Pável, et al.
(författare)
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Taxon cycle predictions supported by model-based inference in Indo-Pacific trap-jaw ants (Hymenoptera: Formicidae: Odontomachus)
- 2018
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Ingår i: Molecular Ecology. - : Wiley. - 0962-1083 .- 1365-294X. ; 27:20, s. 4090-4107
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Tidskriftsartikel (refereegranskat)abstract
- Nonequilibrium dynamics and non-neutral processes, such as trait-dependent dispersal, are often missing from quantitative island biogeography models despite their potential explanatory value. One of the most influential nonequilibrium models is the taxon cycle, but it has been difficult to test its validity as a general biogeographical framework. Here, we test predictions of the taxon cycle model using six expected phylogenetic patterns and a time-calibrated phylogeny of Indo-Pacific Odontomachus (Hymenoptera: Formicidae: Ponerinae), one of the ant genera that E.O. Wilson used when first proposing the hypothesis. We used model-based inference and a newly developed trait-dependent dispersal model to jointly estimate ancestral biogeography, ecology (habitat preferences for forest interiors, vs. marginal habitats, such as savannahs, shorelines, disturbed areas) and the linkage between ecology and dispersal rates. We found strong evidence that habitat shifts from forest interior to open and disturbed habitats increased macroevolutionary dispersal rate. In addition, lineages occupying open and disturbed habitats can give rise to both island endemics re-occupying only forest interiors and taxa that re-expand geographical ranges. The phylogenetic predictions outlined in this study can be used in future work to evaluate the relative weights of neutral (e.g., geographical distance and area) and non-neutral (e.g., trait-dependent dispersal) processes in historical biogeography and community ecology.
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| 3. |
- Wan, Guihong, et al.
(författare)
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Development and validation of time-to-event models to predict metastatic recurrence of localized cutaneous melanoma
- 2024
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Ingår i: Journal of the American Academy of Dermatology. - 0190-9622. ; 90:2, s. 288-298
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Tidskriftsartikel (refereegranskat)abstract
- Background: The recent expansion of immunotherapy for stage IIB/IIC melanoma highlights a growing clinical need to identify patients at high risk of metastatic recurrence and, therefore, most likely to benefit from this therapeutic modality. Objective: To develop time-to-event risk prediction models for melanoma metastatic recurrence. Methods: Patients diagnosed with stage I/II primary cutaneous melanoma between 2000 and 2020 at Mass General Brigham and Dana-Farber Cancer Institute were included. Melanoma recurrence date and type were determined by chart review. Thirty clinicopathologic factors were extracted from electronic health records. Three types of time-to-event machine-learning models were evaluated internally and externally in the distant versus locoregional/nonrecurrence prediction. Results: This study included 954 melanomas (155 distant, 163 locoregional, and 636 1:2 matched nonrecurrences). Distant recurrences were associated with worse survival compared to locoregional/nonrecurrences (HR: 6.21, P < .001) and to locoregional recurrences only (HR: 5.79, P < .001). The Gradient Boosting Survival model achieved the best performance (concordance index: 0.816; time-dependent AUC: 0.842; Brier score: 0.103) in the external validation. Limitations: Retrospective nature and cohort from one geography. Conclusions: These results suggest that time-to-event machine-learning models can reliably predict the metastatic recurrence from localized melanoma and help identify high-risk patients who are most likely to benefit from immunotherapy.
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| 4. |
- Alkabani, Yousra, 1981-, et al.
(författare)
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OE-CAM : A Hybrid Opto-Electronic Content Addressable Memory
- 2020
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Ingår i: IEEE Photonics Journal. - Piscataway, NJ : IEEE. - 1943-0655. ; 12:2
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Tidskriftsartikel (refereegranskat)abstract
- A content addressable memory (CAM) is a type of memory that implements a parallel search engine at its core. A CAM takes as an input a value and outputs the address where this value is stored in case of a match. CAMs are used in a wide range of applications including networking, cashing, neuromorphic associative memories, multimedia, and data analytics. Here, we introduce a novel opto-electronic CAM (OE-CAM) utilizing the integrated silicon photonic platform. In our approach, we explore the performance of an experimental OE-CAM and verify the efficiency of the device at 25 Gbit/s while maintaining the bit integrity under noise conditions. We show that OE-CAM enables a) two orders of magnitude faster search functionality resulting in b) a five orders of magnitude lower power-delay-product compared to CAMs implementations based on other emerging technologies. This remarkable performance potential is achieved by utilizing i) a high parallelism of wavelength-division-multiplexing in the optical domain, combined with ii) 10's of GHz-fast opto-electronic components, packaged in iii) integrated photonics for 10-100's ps-short communication delays. We further verify the upper optical input power limit of this OE-CAM to be given by parasitic nonlinearities inside the silicon waveguides, and the minimal detectable optical power at the back-end photoreceiver's responsivity given channel noise. Such energy-efficient and short-delay OE-CAMs could become a key component of functional photonic-augmented ASICS, co-processors, or smart sensors. © IEEE
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| 5. |
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| 6. |
- Gross, Sean M., et al.
(författare)
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A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses
- 2022
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Ingår i: Communications Biology. - : Springer Nature. - 2399-3642. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.
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| 7. |
- Haugg, Amelie, et al.
(författare)
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Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.
- 2021
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Ingår i: NeuroImage. - : Elsevier. - 1053-8119 .- 1095-9572. ; 237
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Tidskriftsartikel (refereegranskat)abstract
- Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.
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| 8. |
- Peng, Jiaxin, et al.
(författare)
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DNNARA : A Deep Neural Network Accelerator using Residue Arithmetic and Integrated Photonics
- 2020
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Ingår i: Proceedings of the 49th International Conference on Parallel Processing. - New York : Association for Computing Machinery (ACM). - 9781450388160 ; , s. 1-11
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Konferensbidrag (refereegranskat)abstract
- Deep Neural Networks (DNNs) are currently used in many fields, including critical real-time applications. Due to its compute-intensive nature, speeding up DNNs has become an important topic in current research. We propose a hybrid opto-electronic computing architecture targeting the acceleration of DNNs based on the residue number system (RNS). In this novel architecture, we combine the use of Wavelength Division Multiplexing (WDM) and RNS for efficient execution. WDM is used to enable a high level of parallelism while reducing the number of optical components needed to decrease the area of the accelerator. Moreover, RNS is used to generate optical components with short optical critical paths. In addition to speed, this has the advantage of lowering the optical losses and reducing the need for high laser power. Our RNS compute modules use one-hot encoding and thus enable fast switching between the electrical and optical domains. In this work, we demonstrate how to implement the different DNN computational kernels using WDM-enabled RNS based integrated photonics. We provide an accelerator architecture that uses our designed components and perform design space exploration to select efficient architecture parameters. Compared to memristor crossbars, our residue matrix-vector multiplication unit has two orders of magnitude higher peak performance. Our experimental evaluation using DNN benchmarks illustrates that our architecture can perform more than 19 times faster than the state of the art GPUs under the same power budget. © 2020 ACM.
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