Effect of a structurally modified human granulocyte colony stimulating factor, G-CSFa, on leukopenia in mice and monkeys

<p>Abstract</p> <p>Background</p> <p>Granulocyte colony stimulating factor (G-CSF) regulates survival, proliferation, and differentiation of neutrophilic granulocyte precursors, Recombinant G-CSF has been used for the treatment of congenital and therapy-induced neutropenia and stem cell mobilization. Due to its intrinsic instability, recombinant G-CSF needs to be excessively and/or frequently administered to patients in order to maintain a plasma concentration high enough to achieve therapeutic effects. Therefore, there is a need for the development of G-CSF derivatives that are more stable and active in vivo.</p> <p>Methods</p> <p>Using site-direct mutagenesis and recombinant DNA technology, a structurally modified derivative of human G-CSF termed G-CSFa was obtained. G-CSFa contains alanine 17 (instead of cysteine 17 as in wild-type G-CSF) as well as four additional amino acids including methionine, arginine, glycine, and serine at the amino-terminus. Purified recombinant G-CSFa was tested for its in vitro activity using cell-based assays and in vivo activity using both murine and primate animal models.</p> <p>Results</p> <p>In vitro studies demonstrated that G-CSFa, expressed in and purified from <it>E. coli</it>, induced a much higher proliferation rate than that of wild-type G-CSF at the same concentrations. In vivo studies showed that G-CSFa significantly increased the number of peripheral blood leukocytes in cesium-137 irradiated mice or monkeys with neutropenia after administration of clyclophosphamide. In addition, G-CSFa increased neutrophil counts to a higher level in monkeys with a concomitant slower declining rate than that of G-CSF, indicating a longer half-life of G-CSFa. Bone marrow smear analysis also confirmed that G-CSFa was more potent than G-CSF in the induction of granulopoiesis in bone marrows of myelo-suppressed monkeys.</p> <p>Conclusion</p> <p>G-CSFa, a structurally modified form of G-CSF, is more potent in stimulating proliferation and differentiation of myeloid cells of the granulocytic lineage than the wild-type counterpart both in vitro and in vivo. G-CSFa can be explored for the development of a new generation of recombinant therapeutic drug for leukopenia.</p

Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring

Fusing satellite imagery acquired with different sensors has been a long-standing challenge of Earth observation, particularly across different modalities such as optical and synthetic aperture radar (SAR) images. Here, we explore the joint analysis of imagery from different sensors in the light of representation learning: we propose to learn a joint embedding of multiple satellite sensors within a deep neural network. Our application problem is the monitoring of lake ice on Alpine lakes. To reach the temporal resolution requirement of the Swiss Global Climate Observing System (GCOS) office, we combine three image sources: Sentinel-1 SAR (S1-SAR), Terra moderate resolution imaging spectroradiometer (MODIS), and Suomi-NPP visible infrared imaging radiometer suite (VIIRS). The large gaps between the optical and SAR domains and between the sensor resolutions make this a challenging instance of the sensor fusion problem. Our approach can be classified as a late fusion that is learned in a data-driven manner. The proposed network architecture has separate encoding branches for each image sensor, which feed into a single latent embedding, i.e., a common feature representation shared by all inputs, such that subsequent processing steps deliver comparable output irrespective of which sort of input image was used. By fusing satellite data, we map lake ice at a temporal resolution of 91% [respectively, mean per-class Intersection-over-Union (mIoU) scores >60%] and generalizes well across different lakes and winters. Moreover, it sets a new state-of-the-art for determining the important ice-on and ice-off dates for the target lakes, in many cases meeting the GCOS requirement

Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring

Fusing satellite imagery acquired with different sensors has been a long-standing challenge of Earth observation, particularly across different modalities such as optical and Synthetic Aperture Radar (SAR) images. Here, we explore the joint analysis of imagery from different sensors in the light of representation learning: we propose to learn a joint embedding of multiple satellite sensors within a deep neural network. Our application problem is the monitoring of lake ice on Alpine lakes. To reach the temporal resolution requirement of the Swiss Global Climate Observing System (GCOS) office, we combine three image sources: Sentinel-1 SAR (S1-SAR), Terra MODIS, and Suomi-NPP VIIRS. The large gaps between the optical and SAR domains and between the sensor resolutions make this a challenging instance of the sensor fusion problem. Our approach can be classified as a late fusion that is learned in a data-driven manner. The proposed network architecture has separate encoding branches for each image sensor, which feed into a single latent embedding. I.e., a common feature representation shared by all inputs, such that subsequent processing steps deliver comparable output irrespective of which sort of input image was used. By fusing satellite data, we map lake ice at a temporal resolution of < 1.5 days. The network produces spatially explicit lake ice maps with pixel-wise accuracies > 91% (respectively, mIoU scores > 60%) and generalises well across different lakes and winters. Moreover, it sets a new state-of-the-art for determining the important ice-on and ice-off dates for the target lakes, in many cases meeting the GCOS requirement

Mixture of Experts with Uncertainty Voting for Imbalanced Deep Regression Problems

Data imbalance is ubiquitous when applying machine learning to real-world problems, particularly regression problems. If training data are imbalanced, the learning is dominated by the densely covered regions of the target distribution, consequently, the learned regressor tends to exhibit poor performance in sparsely covered regions. Beyond standard measures like over-sampling or re-weighting, there are two main directions to handle learning from imbalanced data. For regression, recent work relies on the continuity of the distribution; whereas for classification there has been a trend to employ mixture-of-expert models and let some ensemble members specialize in predictions for the sparser regions. Here, we adapt the mixture-of-experts approach to the regression setting. A main question when using this approach is how to fuse the predictions from multiple experts into one output. Drawing inspiration from recent work on probabilistic deep learning, we propose to base the fusion on the aleatoric uncertainties of individual experts, thus obviating the need for a separate aggregation module. In our method, dubbed MOUV, each expert predicts not only an output value but also its uncertainty, which in turn serves as a statistically motivated criterion to rely on the right experts. We compare our method with existing alternatives on multiple public benchmarks and show that MOUV consistently outperforms the prior art, while at the same time producing better calibrated uncertainty estimates. Our code is available at link-upon-publication

Accuracy and Consistency of Space-based Vegetation Height Maps for Forest Dynamics in Alpine Terrain

Monitoring and understanding forest dynamics is essential for environmental conservation and management. This is why the Swiss National Forest Inventory (NFI) provides countrywide vegetation height maps at a spatial resolution of 0.5 m. Its long update time of 6 years, however, limits the temporal analysis of forest dynamics. This can be improved by using spaceborne remote sensing and deep learning to generate large-scale vegetation height maps in a cost-effective way. In this paper, we present an in-depth analysis of these methods for operational application in Switzerland. We generate annual, countrywide vegetation height maps at a 10-meter ground sampling distance for the years 2017 to 2020 based on Sentinel-2 satellite imagery. In comparison to previous works, we conduct a large-scale and detailed stratified analysis against a precise Airborne Laser Scanning reference dataset. This stratified analysis reveals a close relationship between the model accuracy and the topology, especially slope and aspect. We assess the potential of deep learning-derived height maps for change detection and find that these maps can indicate changes as small as 250 $m^2$. Larger-scale changes caused by a winter storm are detected with an F1-score of 0.77. Our results demonstrate that vegetation height maps computed from satellite imagery with deep learning are a valuable, complementary, cost-effective source of evidence to increase the temporal resolution for national forest assessments

SemDpray: Virtual reality as-is semantic information labeling tool for 3D spatial data

Capturing the as-is status of buildings in the form of 3D spatial data has been becoming more accurate and efficient, but the act of extracting from it as-is information has not seen similar advancements. State-of-the-art practice requires experts to manually interact with the spatial data in a laborious and time-consuming process. We propose Semantic Spray (Semspray), a Virtual Reality (VR) application that provides users with intuitive tools to produce semantic information on as-is 3D spatial data of buildings. The goal is to perform this task accurately and more efficiently by allowing users to interact with the data at different scales

Smoothly Transitive Fixed Frequency Hysteresis Current Control Based on Optimal Voltage Space Vector

This paper proposes a smoothly transitive fixed frequency hysteresis current control (ST-FHCC) scheme applied to an active power filter (APF). First of all, a switching fixed frequency hysteresis current control (S-FHCC) is introduced, which is based on phase-to-phase decoupling and switching the control strategies under mode 0 or mode 1, and its weakness is described in detail. To enhance it, an improved approach of regulating the hysteresis bandwidth is presented to fix the switching frequency with switch phases being regulated, based on the optimal voltage space vector (OVSV). Furthermore, a flexible division of the voltage-space-vectors diagram is developed to divide the original voltage-space-vectors diagram into six sub-regions, upon which the control strategies under mode 0 and mode 1 can be switched alternately in order to obtain a smooth transition. As a consequence, ST-FHCC can thoroughly avoid the inherent weakness of S-FHCC of switching that is not smooth as a result of the low control accuracy of current errors. Case studies are carried out through power systems computer aided design/electromagnetic transients including DC (PSCAD/EMTDC) while simulation results verify the effectiveness and superiority of ST-FHCC compared to S-FHCC

Accuracy and consistency of space-based vegetation height maps for forest dynamics in alpine terrain

Monitoring and understanding forest dynamics is essential for environmental conservation and management. This is why the Swiss National Forest Inventory (NFI) provides countrywide vegetation height maps at a spatial resolution of 0.5 m. Its long update time of 6 years, however, limits the temporal analysis of forest dynamics. This can be improved by using spaceborne remote sensing and deep learning to generate large-scale vegetation height maps in a cost-effective way. In this paper, we present an in-depth analysis of these methods for operational application in Switzerland. We generate annual, countrywide vegetation height maps at a 10-m ground sampling distance for the years 2017–2020 based on Sentinel-2 satellite imagery. In comparison to previous works, we conduct a large-scale and detailed stratified analysis against a precise Airborne Laser Scanning reference dataset. This stratified analysis reveals a close relationship between the model accuracy and the topology, especially slope and aspect. We assess the potential of deep learning-derived height maps for change detection and find that these maps can indicate changes as small as 250 m2. Larger-scale changes caused by a winter storm are detected with an F1-score of 0.77. Our results demonstrate that vegetation height maps computed from satellite imagery with deep learning are a valuable, complementary, cost-effective source of evidence to increase the temporal resolution for national forest assessments.ISSN:2666-017

Adjust band gap of IATO nanoparticles to obtain desirable optical property by one-step hydrothermal oxidation

Antimony-tin-doped indium oxide (IATO) as transparent conducting oxide (TCO) exhibits significant optical property on blocking UV and Infrared(IR) for wavelengths less ∼400 nm and over ∼1400 nm as well as appropriate transmissivity on visible wavelength in our work that can be as an optional idea optical material applying in shielding film or nanocomposite to achieve desired optical application. We have successfully developed an optimal synthesis system which allows for a single hydrothermal oxidation directly synthesizing IATO nanoparticles without high-temperature calcination. These nanoparticles show superior size, crystallinity, agglomeration and are free of intermediates In(OH)3 and InOOH. We also have demonstrated they give scope to desired optical property as a result of an altered IATO band gap energy. We highlight this approach due to the shortened preparation time, the reduced energy consumption and the decreased chemical usage which dramatically save on production costs and protect environment

Accuracy and consistency of space-based vegetation height maps for forest dynamics in alpine terrain

Monitoring and understanding forest dynamics is essential for environmental conservation and management. This is why the Swiss National Forest Inventory (NFI) provides countrywide vegetation height maps at a spatial resolution of 0.5 m. Its long update time of 6 years, however, limits the temporal analysis of forest dynamics. This can be improved by using spaceborne remote sensing and deep learning to generate large-scale vegetation height maps in a cost-effective way. In this paper, we present an in-depth analysis of these methods for operational application in Switzerland. We generate annual, countrywide vegetation height maps at a 10-m ground sampling distance for the years 2017–2020 based on Sentinel-2 satellite imagery. In comparison to previous works, we conduct a large-scale and detailed stratified analysis against a precise Airborne Laser Scanning reference dataset. This stratified analysis reveals a close relationship between the model accuracy and the topology, especially slope and aspect. We assess the potential of deep learning-derived height maps for change detection and find that these maps can indicate changes as small as 250 m2. Larger-scale changes caused by a winter storm are detected with an F1-score of 0.77. Our results demonstrate that vegetation height maps computed from satellite imagery with deep learning are a valuable, complementary, cost-effective source of evidence to increase the temporal resolution for national forest assessments