731 research outputs found
Spatial cellular architecture predicts prognosis in glioblastoma
Intra-tumoral heterogeneity and cell-state plasticity are key drivers for the
therapeutic resistance of glioblastoma. Here, we investigate the association
between spatial cellular organization and glioblastoma prognosis. Leveraging
single-cell RNA-seq and spatial transcriptomics data, we develop a deep
learning model to predict transcriptional subtypes of glioblastoma cells from
histology images. Employing thismodel, we phenotypically analyze 40 million
tissue spots from 410 patients and identify consistent associations between
tumor architecture and prognosis across two independent cohorts. Patients
with poor prognosis exhibit higher proportions of tumor cells expressing a
hypoxia-induced transcriptional program. Furthermore, a clustering pattern
of astrocyte-like tumor cells is associated with worse prognosis, while dispersion
and connection of the astrocytes with other transcriptional subtypes
correlate with decreased risk. To validate these results, we develop a separate
deep learning model that utilizes histology images to predict prognosis.
Applying this model to spatial transcriptomics data reveal survival-associated
regional gene expression programs. Overall, our study presents a scalable
approach to unravel the transcriptional heterogeneity of glioblastoma and
establishes a critical connection between spatial cellular architecture and
clinical outcomes
Reliability-based cleaning of noisy training labels with inductive conformal prediction in multi-modal biomedical data mining
Accurately labeling biomedical data presents a challenge. Traditional
semi-supervised learning methods often under-utilize available unlabeled data.
To address this, we propose a novel reliability-based training data cleaning
method employing inductive conformal prediction (ICP). This method capitalizes
on a small set of accurately labeled training data and leverages ICP-calculated
reliability metrics to rectify mislabeled data and outliers within vast
quantities of noisy training data. The efficacy of the method is validated
across three classification tasks within distinct modalities: filtering
drug-induced-liver-injury (DILI) literature with title and abstract, predicting
ICU admission of COVID-19 patients through CT radiomics and electronic health
records, and subtyping breast cancer using RNA-sequencing data. Varying levels
of noise to the training labels were introduced through label permutation.
Results show significant enhancements in classification performance: accuracy
enhancement in 86 out of 96 DILI experiments (up to 11.4%), AUROC and AUPRC
enhancements in all 48 COVID-19 experiments (up to 23.8% and 69.8%), and
accuracy and macro-average F1 score improvements in 47 out of 48 RNA-sequencing
experiments (up to 74.6% and 89.0%). Our method offers the potential to
substantially boost classification performance in multi-modal biomedical
machine learning tasks. Importantly, it accomplishes this without necessitating
an excessive volume of meticulously curated training data
Early dietary exposures epigenetically program mammary cancer susceptibility through Igf1-mediated expansion of the mammary stem cell compartment
This article belongs to the Collection Insulin-Like Growth Factors in Development, Cancers and Aging.Diet is a critical environmental factor affecting breast cancer risk, and recent evidence shows that dietary exposures during early development can affect lifetime mammary cancer susceptibility. To elucidate the underlying mechanisms, we used our established crossover feeding mouse model, where exposure to a high-fat and high-sugar (HFHS) diet during defined developmental windows determines mammary tumor incidence and latency in carcinogen-treated mice. Mammary tumor incidence is significantly increased in mice receiving a HFHS post-weaning diet (high-tumor mice, HT) compared to those receiving a HFHS diet during gestation (low-tumor mice, LT). The current study revealed that the mammary stem cell (MaSC) population was significantly increased in mammary glands from HT compared to LT mice. Igf1 expression was increased in mammary stromal cells from HT mice, where it promoted MaSC self-renewal. The increased Igf1 expression was induced by DNA hypomethylation of the Igf1 Pr1 promoter, mediated by a decrease in Dnmt3b levels. Mammary tissues from HT mice also had reduced levels of Igfbp5, leading to increased bioavailability of tissue Igf1. This study provides novel insights into how early dietary exposures program mammary cancer risk, demonstrating that effective dietary intervention can reduce mammary cancer incidenceThe research was supported by institutional funding from Texas A&M University and the Discovery Foundatio
Conceptual design and progress of transmitting MV DC HV into 4 K LHe detectors
A dual-phase TPC (Time Projection Chamber) is more advanced in characterizing
an event than a single-phase one because it can, in principle, reconstruct the
3D (X-Y-Z) image of the event, while a single-phase detector can only show a 2D
(X-Y) picture. As a result, more enriched physics is expected for a dual-phase
detector than a single-phase one. However, to build such a detector, DC HV
(High Voltage) must be delivered into the chamber (to have a static electric
field), which is a challenging task, especially for an LHe detector due to the
extremely low temperature, 4 K, and the very high voltage, MV
(Million Volts). This article introduces a convincing design for transmitting
MV DC into a 4 K LHe detector. We also report the progress of
manufacturing a 100 kV DC feedthrough capable of working at 4 K. Surprisingly,
we realized that the technology we developed here might be a valuable reference
to the scientists and engineers aiming to build residential bases on the Moon
or Mars
Searching for ER and/or NR-like dark matter signals with the especially low background liquid helium TPCs
In the Dark Matter (DM) direct detection community, the absence of convincing
signals has become a ``new normal'' for decades. Among other possibilities, the
``new normal'' might indicate that DM-matter interactions could generate not
only the hypothetical NR (Nuclear Recoil) events but also the ER (Electron
Recoil) ones, which have often been tagged as backgrounds historically.
Further, we argue that ER and NR-like DM signals could co-exist in a DM
detector's same dataset. So in total, there would be three scenarios we can
search for DM signals: (i) ER excess only, (ii) NR excess only, and (iii) ER
and NR excesses combined. To effectively identify any possible DM signal under
the three scenarios, a DM detector should (a) have the minimum ER and NR
backgrounds and (b) be capable of discriminating ER events from NR ones.
Accordingly, we introduce the newly established project, ALETHEIA, which
implements liquid helium-filled TPCs (Time Projection Chamber) in hunting for
DM. Thanks to the nearly single-digit number of ER and NR backgrounds on 1
ton*yr exposure, presumably, the ALETHEIA detectors should be able to identify
any form of DM-induced excess in its ROI (Research Of Interest). As far as we
know, ALETHEIA is the first DM direct detection experiment claiming such an
inclusive search; conventional detectors search DM mainly on the ``ER excess
only'' and/or the ``NR excess only'' channel, not the ``ER and NR excesses
combined'' channel. In addition, we introduce a preliminary scheme to one of
the most challenging R\&D tasks, transmitting 500+ kV into a 4 K LHe detector
Letter of Intent: Jinping Neutrino Experiment
Jinping Neutrino Experiment (Jinping) is proposed to significantly improve
measurements on solar neutrinos and geoneutrinos in China Jinping Laboratory -
a lab with a number of unparalleled features, thickest overburden, lowest
reactor neutrino background, etc., which identify it as the world-best
low-energy neutrino laboratory. The proposed experiment will have target mass
of 4 kilotons of liquid scintillator or water-based liquid scintillator, with a
fiducial mass of 2 kilotons for neutrino-electron scattering events and 3
kilotons for inverse-beta interaction events. A number of initial sensitivities
studies have been carried out, including on the transition phase for the solar
neutrinos oscillation from the vacuum to the matter effect, the discovery of
solar neutrinos from the carbon-nitrogen-oxygen (CNO) cycle, the resolution of
the high and low metallicity hypotheses, and the unambiguous separation on U
and Th cascade decays from the dominant crustal anti-electron neutrinos in
China.Comment: Proposal for the Jinping Neutrino Experimen
Les droits disciplinaires des fonctions publiques : « unification », « harmonisation » ou « distanciation ». A propos de la loi du 26 avril 2016 relative à la déontologie et aux droits et obligations des fonctionnaires
The production of tt‾ , W+bb‾ and W+cc‾ is studied in the forward region of proton–proton collisions collected at a centre-of-mass energy of 8 TeV by the LHCb experiment, corresponding to an integrated luminosity of 1.98±0.02 fb−1 . The W bosons are reconstructed in the decays W→ℓν , where ℓ denotes muon or electron, while the b and c quarks are reconstructed as jets. All measured cross-sections are in agreement with next-to-leading-order Standard Model predictions.The production of , and is studied in the forward region of proton-proton collisions collected at a centre-of-mass energy of 8 TeV by the LHCb experiment, corresponding to an integrated luminosity of 1.98 0.02 \mbox{fb}^{-1}. The bosons are reconstructed in the decays , where denotes muon or electron, while the and quarks are reconstructed as jets. All measured cross-sections are in agreement with next-to-leading-order Standard Model predictions
LHCb upgrade software and computing : technical design report
This document reports the Research and Development activities that are carried out in the software and computing domains in view of the upgrade of the LHCb experiment. The implementation of a full software trigger implies major changes in the core software framework, in the event data model, and in the reconstruction algorithms. The increase of the data volumes for both real and simulated datasets requires a corresponding scaling of the distributed computing infrastructure. An implementation plan in both domains is presented, together with a risk assessment analysis
Physics case for an LHCb Upgrade II - Opportunities in flavour physics, and beyond, in the HL-LHC era
The LHCb Upgrade II will fully exploit the flavour-physics opportunities of the HL-LHC, and study additional physics topics that take advantage of the forward acceptance of the LHCb spectrometer. The LHCb Upgrade I will begin operation in 2020. Consolidation will occur, and modest enhancements of the Upgrade I detector will be installed, in Long Shutdown 3 of the LHC (2025) and these are discussed here. The main Upgrade II detector will be installed in long shutdown 4 of the LHC (2030) and will build on the strengths of the current LHCb experiment and the Upgrade I. It will operate at a luminosity up to 2×1034
cm−2s−1, ten times that of the Upgrade I detector. New detector components will improve the intrinsic performance of the experiment in certain key areas. An Expression Of Interest proposing Upgrade II was submitted in February 2017. The physics case for the Upgrade II is presented here in more depth. CP-violating phases will be measured with precisions unattainable at any other envisaged facility. The experiment will probe b → sl+l−and b → dl+l− transitions in both muon and electron decays in modes not accessible at Upgrade I. Minimal flavour violation will be tested with a precision measurement of the ratio of B(B0 → μ+μ−)/B(Bs → μ+μ−). Probing charm CP violation at the 10−5 level may result in its long sought discovery. Major advances in hadron spectroscopy will be possible, which will be powerful probes of low energy QCD. Upgrade II potentially will have the highest sensitivity of all the LHC experiments on the Higgs to charm-quark couplings. Generically, the new physics mass scale probed, for fixed couplings, will almost double compared with the pre-HL-LHC era; this extended reach for flavour physics is similar to that which would be achieved by the HE-LHC proposal for the energy frontier
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