Computational cytometer based on magnetically modulated coherent imaging and deep learning.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications

Lizzy0Sun/DHSVM: DHSVM v3.3

Source code and sample test files for the DHSVM-PNNL hydrological mode

Next-Generation Intensity-Duration-Frequency Curves for Diverse Land across the Continental United States

Abstract The current methods for designing hydrological infrastructure rely on precipitation-based intensity-duration-frequency curves. However, they cannot accurately predict flooding caused by snowmelt or rain-on-snow events, potentially leading to underdesigned infrastructure and property damage. To address these issues, next-generation intensity-duration-frequency (NG-IDF) curves have been developed for the open condition, characterizing water available for runoff from rainfall, snowmelt, and rain-on-snow. However, they lack consideration of land use land cover (LULC) factors, which can significantly affect runoff processes. We address this limitation by expanding open area NG-IDF dataset to include eight vegetated LULCs over the continental United States, including forest (deciduous, evergreen, mixed), shrub, grass, pasture, crop, and wetland. This NG-IDF 2.0 dataset offers a comprehensive analysis of hydrological extreme events and their associated drivers under different LULCs at a continental scale. It will serve as a useful resource for improving standard design practices and aiding in the assessment of infrastructure design risks. Additionally, it provides useful insights into how changes in LULC impact flooding magnitude, mechanisms, timing, and snow water supply

Informing climate adaptation strategies using ecological simulation models and spatial decision support tools

IntroductionForest landscapes offer resources and ecosystem services that are vital to the social, economic, and cultural well-being of human communities, but managing for these provisions can require socially and ecologically relevant trade-offs. We designed a spatial decision support model to reveal trade-offs and synergies between ecosystem services in a large eastern Cascade Mountain landscape in Washington State, USA.MethodsWe used process-based forest landscape (LANDIS-II) and hydrology (DHSVM) models to compare outcomes associated with 100 years of simulated forest and wildfire dynamics for two management scenarios, Wildfire only and Wildfire + Treatments. We then examined the strength and spatial distribution of potential treatment effects and trends in a set of resources and ecosystem services over the simulation period.ResultsWe found that wildfire area burned increased over time, but some impacts could be mitigated by adaptation treatments. Treatment benefits were not limited to treated areas. Interestingly, we observed neighborhood benefits where fire spread and severity were reduced not only in treated patches but in adjacent patches and landscapes as well, creating potential synergies among some resource benefits and services. Ordinations provided further evidence for two main kinds of outcomes. Positive ecological effects of treatments were greatest in upper elevation moist and cold forests, while positive benefits to human communities were aligned with drier, low- and mid-elevation forests closer to the wildland urban interface.DiscussionOur results contribute to improved understanding of synergies and tradeoffs linked to adaptation and restoration efforts in fire-prone forests and can be used to inform management aimed at rebuilding resilient, climate-adapted landscapes

A Nanobody Against Cytotoxic T-Lymphocyte Associated Antigen-4 Increases the Anti-Tumor Effects of Specific CD8+ T Cells

Adoptive cell-based immunotherapy typically utilizes cytotoxic T lymphocytes (CTLs), expanding these cells ex vivo. Such expansion is traditionally accomplished through the use of autologous APCs that are capable of interactions with T cells. However, incidental inhibitory program such as CTLA-4 pathway can impair T cell proliferation. We therefore designed a nanobody which is specific for CTLA-4 (CTLA-4 Nb 16), and we then used this molecule to assess its ability to disrupt CTLA-4 signaling and thereby overcome negative costimulation of T cells. With CTLA-4 Nb16 stimulation, dendritic cell/hepatocellular carcinoma fusion cells (DC/HepG2-FCs) enhanced autologous CD8+ T cell proliferation and production of IFN-γ in vitro, thereby leading to enhanced killing of tumor cells. Using this approach in the context of adoptive CD8+ immunotherapy led to a marked suppression of tumor growth in murine NOD/SCID hepatocarcinoma or breast cancer xenograft models. We also observed significantly increased tumor cell apoptosis, and corresponding increases in murine survival. These findings thus demonstrate that in response to nanobody stimulation, DC/tumor cells-FC-induced specific CTLs exhibit superior anti-tumor efficacy, making this a potentially valuable means of achieving better adoptive immunotherapy outcomes in cancer patients

A novel construct for scaling groundwater–river interactions based on machine-guided hydromorphic classification

Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. Predictive numerical models are needed to understand responses of river corridors to environmental change and to support sustainable watershed management. We posit that systematic hydromorphic classification provides a scaling construct that facilitates extrapolation of outputs from local-scale mechanistic models to reduced-order models applicable at reach and watershed scales. This in turn offers the potential to improve large-scale predictions of river corridor hydrobiogeochemical processes. Here we present a new machine-guided hydromorphic classification methodology that addresses the key requirements of this objective, and we demonstrate its application to a segment of the Columbia River in the northwestern United States. The resulting hydromorphic classes form spatially coherent and physically interpretable hydromorphic units that exhibit distinct behaviors in terms of distributions of subsurface transit times (a primary control on critical biogeochemical reactions). This approach forms the basis of ongoing research that is evaluating the formulation of reduced-order models and transferability of results to other river reaches and larger scales

Nanobody-based chimeric antigen receptor T cells designed by CRISPR/Cas9 technology for solid tumor immunotherapy.

Chimeric antigen receptor-based T-cell immunotherapy is a promising strategy for treatment of hematological malignant tumors; however, its efficacy towards solid cancer remains challenging. We therefore focused on developing nanobody-based CAR-T cells that treat the solid tumor. CD105 expression is upregulated on neoangiogenic endothelial and cancer cells. CD105 has been developed as a drug target. Here we show the generation of a CD105-specific nanobody, an anti-human CD105 CAR-T cells, by inserting the sequences for anti-CD105 nanobody-linked standard cassette genes into AAVS1 site using CRISPR/Cas9 technology. Co-culture with CD105+ target cells led to the activation of anti-CD105 CAR-T cells that displayed the typically activated cytotoxic T-cell characters, ability to proliferate, the production of pro-inflammatory cytokines, and the specific killing efficacy against CD105+ target cells in vitro. The in vivo treatment with anti-CD105 CAR-T cells significantly inhibited the growth of implanted CD105+ tumors, reduced tumor weight, and prolonged the survival time of tumor-bearing NOD/SCID mice. Nanobody-based CAR-T cells can therefore function as an antitumor agent in human tumor xenograft models. Our findings determined that the strategy of nanobody-based CAR-T cells engineered by CRISPR/Cas9 system has a certain potential to treat solid tumor through targeting CD105 antigen

Genomic structural variation is associated with hypoxia adaptation in high-altitude zokors

Zokors, an Asiatic group of subterranean rodents, originated in lowlands and colonized high-elevational zones following the uplift of the Qinghai-Tibet plateau about 3.6 million years ago. Zokors live at high elevation in subterranean burrows and experience hypobaric hypoxia, including both hypoxia (low oxygen concentration) and hypercapnia (elevated partial pressure of CO2). Here we report a genomic analysis of six zokor species (genus Eospalax) with different elevational ranges to identify structural variants (deletions and inversions) that may have contributed to high-elevation adaptation. Based on an assembly of a chromosome-level genome of the high-elevation species, Eospalax baileyi, we identified 18 large inversions that distinguished this species from congeners native to lower elevations. Small-scale structural variants in the introns of EGLN1, HIF1A, HSF1 and SFTPD of E. baileyi were associated with the upregulated expression of those genes. A rearrangement on chromosome 1 was associated with altered chromatin accessibility, leading to modified gene expression profiles of key genes involved in the physiological response to hypoxia. Multigene families that underwent copy-number expansions in E. baileyi were enriched for autophagy, HIF1 signalling and immune response. E. baileyi show a significantly larger lung mass than those of other Eospalax species. These findings highlight the key role of structural variants underlying hypoxia adaptation of high-elevation species in Eospalax.</p

Computational cytometer based on magnetically modulated coherent imaging and deep learning.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications