22 research outputs found
Correction to “Ultrafast Cation Exchange in Supra-Quantum Dots through Nanoporous Internal Structure”
No full textThe author list should be “Hyunmi Doh, Juwon Park, Junhwa Lee, Jiwon Bang, Sanghwa Jeong, Wonseok Lee, Ho Jin, and Sungjee Kim”, with an addition of a co-author, Junhwa Lee. Hyunmi Doh, Juwon Park, and Junhwa Lee contributed equally to this work. This change was agreed to by all authors and is reflected in the authorship of this correction.11Nsciescopu
Simple methods to determine the dissociation constant, Kd
No full textThe determination of the dissociation constant (K-d) is pivotal in biochemistry and pharmacology for understanding binding affinities in chemical reactions, which is crucial for drug development and comprehending biological systems. Here, we introduce a single-molecule fluorescence resonance energy transfer-based method for determining K-d, alongside the conventional electrophoretic mobility shift assay method of K-d, offering insights into thermodynamic interactions between proteins and substrates. The single-molecule fluorescence resonance energy transfer approach is highlighted for its ability to accurately measure binding and dissociation kinetics through fluorescence labeling and the intrinsic nature of protein-DNA interactions, representing a significant advancement in the fields of molecular biology and pharmacology. (c) 2024 The Author(s). Published by Elsevier Inc. on behalf of Korean Society for Molecular and Cellular Biology.
지표면이 이질적인 농경지를 대상으로 영상합성자료와 현장스펙트럼측정 간의 상호비교
No full text학위논문 (석사)-- 서울대학교 대학원 : 농업생명과학대학 생태조경·지역시스템공학부(생태조경학), 2019. 2. 류영렬.다양한 규모의 생태계 역동성을 관찰하기 위해 중요한 시공간 해상도가 높은 영상에 대한 기술의 발전과 함께 그 정확도 평가가 요구된다. 기존의 영상합성 자료를 평가하는 연구들은 지표면이 이질적인 경관에서 체계적인 평가를 하기 알맞은 픽셀단위의 현장 스펙트럼 측정값이 결여되었다. 본 연구는 식물의 생장기 동안 지표면이 이질적인 벼논 경관에서 현존하는 네가지 영상합성 자료- ESTARFM와 FSDAF, STAIR, CESTEM -를 현장 스펙트럼 측정값과 상호비교하였다. 영상합성의 NDV (정규식생지수)가 시공간적으로 변화하는 것에 대해 실험하였다. 영상합성의 NDVI는 현장 NDVI에 대해 알맞거나 높은 선형성을 보였고, 그 값들이 음의 방향으로 편향되었다 (0.73< R2 <0.93, -8 %<bias <-1.6%). 영상합성들의 NDVI는 NDVI 변동성을 포착하였지만, 현장 NDVI보다 낮은 변동성을 예측하였다. 이러한 결과로 현장자료에 비해 영상합성에 입력하는 자료의 값이 편향된 경우 영상합성 자료에 주는 영향은 표지피복의 분류에 따라 다르다는 것을 도출하였다. 또한 영상합성의 NDVI자료는 식생활동의 변동성을 과소평가 할 수 있음을 알 수 있었다. 기존의 연구결과와 달리 ESTARFM과 FSDAF, STAIR는 입력자료에 대한 시간적 의존도가 명확하게 관찰되지 않은 토지피복이 있었다. 현장 NDVI와 비교를 통해 각 영상합성의 NDVI의 강점을 확증하였다. 본 연구에 사용된 현장 스펙트럼 자료는 영상합성 자료의 불확실성을 평가하고, 경작지를 모니터링하기 적합한 영상자료를 향상시키고, 연구자의 목적에 맞게 영상합성기법을 선택하기에 유용할 것으로 전망된다.The advances from high spatiotemporal resolution images that are important for the monitoring of ecosystem dynamics across different scales demand the accuracy assessment. Previous evaluation studies of fusion products lack pixel-level in-situ spectral measurements that fit the systematic evaluation in a heterogeneous landscape. We conducted an inter-comparison of existing four image fusion products with systematically collected in-situ spectral measurements over heterogeneous rice paddy landscape during the whole growing season. The four image fusion products include enhanced spatial-temporal adaptive reflectance fusion model (ESTARFM), flexible spatiotemporal data fusion (FSDAF), satellite data integration (STAIR), and CubeSat enabled spatiotemporal enhancement method (CESTEM). We tested fusion NDVI products, Normalized difference vegetation index, in terms of spatial patterns and temporal variations. Fusion NDVI products showed the moderate to strong linear relationships and negative bias against ground NDVI (R2 range 0.73 to 0.93, bias up to -11%). Although fusion NDVI products captured the NDVI variation, ground NDVI had a larger amplitude than fusion NDVI products. The results indicated that the positive bias of input against in-situ measurement caused the overestimation on mixed land cover type, and the negative bias of input against in-situ measurement caused the underestimation on homogeneous rice paddy cover type. Furthermore, fusion NDVI products could underestimate the variations in vegetation activity. The temporal dependency of ESTARFM, FSDAF, and STAIR was not clearly detected on the mixed land cover type. By comparing fusion NDVI products to ground NDVI, the strength of each fusion product was confirmed. We expect our in-situ spectral measurement could be useful in quantifying uncertainty in image fusion products, improving fine resolution cropland mapping and monitoring, and choosing the algorithm depending on the research purpose.Abstract i
Table of Contents iii
List of Figures v
List of Tables vi
1. Introduction 1
2. Method 6
2.1 Study Site 6
2.2 Image Fusion Algorithms 7
2.3 Satellite Data 10
2.4 Ground Measurements 11
2.5 Evaluation 15
3. Result 16
3.1 Comparison of Satellite NDVI Products Against Ground NDVI 16
3.2 Comparison of Fusion NDVI Products Against Ground NDVI 17
3.3 Time-series of NDVI on Each Plot 21
4. Discussion 24
4.1 Confirming the Strengths of Each Fusion Product with Ground Measurements 24
4.2 Evaluation of Input Data 25
4.3 How the Bias of Input Data Against Ground Measurements Affect Final Fusion Products? 26
4.4 Does Time Lag between Input-pair Date and Predicted Data Always Affect the Fusion Performance? 28
4.5 Fusion NDVI Products for Detecting Vegetation Activity 29
4.6 Dataset for Assessment Accuracy 29
5. Conclusion 31
Reference 32
Supplementary Materials 36
국문 초록 41Maste
시공간 해상도 향상을 통한 식생 변화 모니터링
No full text학위논문(박사) -- 서울대학교대학원 : 환경대학원 협동과정 조경학, 2023. 2. 류영렬.육상 생태계에서 대기권과 생물권의 상호 작용을 이해하기 위해서는 식생 변화의 모니터링이 필요하다. 이 때, 위성영상은 지표면을 관측하여 식생지도를 제공할 수 있지만, 지표변화의 상세한 정보는 구름이나 위성 이미지의 공간 해상도에 의해 제한되었다. 또한 위성영상의 시공간 해상도가 식생지도를 통한 광합성 모니터링에 미치는 영향은 완전히 밝혀지지 않았다.
본 논문에서는 고해상도 식생 지도를 일단위로 생성하기 위성 영상의 시공간 해상도를 향상시키는 것을 목표로 하였다. 고해상도 위성영상을 활용한 식생 변화 모니터링을 시공간적으로 확장하기 위해 1) 정지궤도 위성을 활용한 영상융합을 통해 시간해상도 향상, 2) 적대적생성네트워크를 활용한 공간해상도 향상, 3) 시공간해상도가 높은 위성영상을 토지피복이 균질하지 않은 공간에서 식물 광합성 모니터링을 수행하였다. 이처럼, 위성기반 원격탐지에서 새로운 기술이 등장함에 따라 현재 및 과거의 위성영상은 시공간 해상도 측면에서 향상되어 식생 변화의 모니터링 할 수 있다.
제2장에서는 정지궤도위성영상을 활용하는 시공간 영상융합으로 식물의 광합성을 모니터링 했을 때, 시간해상도가 향상됨을 보였다. 시공간 영상융합 시, 구름탐지, 양방향 반사 함수 조정, 공간 등록, 시공간 융합, 시공간 결측치 보완 등의 과정을 거친다. 이 영상융합 산출물은 경작관리 등으로 식생 지수의 연간 변동이 큰 두 장소(농경지와 낙엽수림)에서 평가하였다. 그 결과, 시공간 영상융합 산출물은 결측치 없이 현장관측을 예측하였다 (R2 = 0.71, 상대 편향 = 5.64% 농경지; R2 = 0.79, 상대 편향 = -13.8%, 활엽수림). 시공간 영상융합은 식생 지도의 시공간 해상도를 점진적으로 개선하여, 식물 생장기동안 위성영상이 현장 관측을 과소 평가를 줄였다. 영상융합은 높은 시공간 해상도로 광합성 지도를 일간격으로 생성하기에 이를 활용하여 위성 영상의 제한된 시공간 해상도로 밝혀지지 않은 식물변화의 과정을 발견하길 기대한다.
식생의 공간분포은 정밀농업과 토지 피복 변화 모니터링을 위해 필수적이다. 고해상도 위성영상으로 지구 표면을 관측하는 것을 용이하게 해졌다. 특히 Planet Fusion은 초소형위성군 데이터를 최대한 활용해 데이터 결측이 없는 3m 공간 해상도의 지표 표면 반사도이다. 그러나 과거 위성 센서(Landsat의 경우 30~60m)의 공간 해상도는 식생의 공간적 변화를 상세 분석하는 것을 제한했다. 제3장에서는 Landsat 데이터의 공간 해상도를 향상하기 위해 Planet Fusion 및 Landsat 8 데이터를 사용하여 이중 적대적 생성 네트워크(the dual RSS-GAN)를 학습시켜, 고해상도 정규화 식생 지수(NDVI)와 식물 근적외선 반사(NIRv)도를 생성하는 한다. 타워기반 현장 식생지수(최대 8년)와 드론기반 초분광지도로 the dual RSS-GAN의 성능을 대한민국 내 두 대상지(농경지와 활엽수림)에서 평가했다. The dual RSS-GAN은 Landsat 8 영상의 공간해상도를 향상시켜 공간 표현을 보완하고 식생 지수의 계절적 변화를 포착했다(R2> 0.96). 그리고 the dual RSS-GAN은 Landsat 8 식생 지수가 현장에 비해 과소 평가되는 것을 완화했다. 현장 관측에 비해 이중 RSS-GAN과 Landsat 8의 상대 편향 값 각각 -0.8% 에서 -1.5%, -10.3% 에서 -4.6% 였다. 이러한 개선은 Planet Fusion의 공간정보를 이중 RSS-GAN로 학습하였기에 가능했다. 헤당 연구 결과는 Landsat 영상의 공간 해상도를 향상시켜 숨겨진 공간 정보를 제공하는 새로운 접근 방식이다.
고해상도에서 식물 광합성 지도는 토지피복이 복잡한 공간에서 탄소 순환 모니터링시 필수적이다. 그러나 Sentinel-2, Landsat 및 MODIS와 같이 태양 동조 궤도에 있는 위성은 공간 해상도가 높거나 시간 해상도 높은 위성영상만 제공할 수 있다. 최근 발사된 초소형위성군은 이러한 해상도 한계을 극복할 수 있다. 특히 Planet Fusion은 초소형위성 자료의 시공간 해상도로 지표면을 관측할 수 있다. 4장에서, Planet Fusion 지표반사도를 이용하여 식생에서 반사된 근적외선 복사(NIRvP)를 3m 해상도 지도를 일간격으로 생성했다. 그런 다음 미국 캘리포니아주 새크라멘토-샌 호아킨 델타의 플럭스 타워 네트워크 데이터와 비교하여 식물 광합성을 추정하기 위한 NIRvP 지도의 성능을 평가하였다. 전체적으로 NIRvP 지도는 습지의 잦은 수위 변화에도 불구하고 개별 대상지의 식물 광합성의 시간적 변화를 포착하였다. 그러나 대상지 전체에 대한 NIRvP 지도와 식물 광합성 사이의 관계는 NIRvP 지도를 플럭스 타워 관측범위와 일치시킬 때만 높은 상관관계를 보였다. 관측범위를 일치시킬 경우, NIRvP 지도는 식물 광합성을 추정하는 데 있어 현장 NIRvP보다 우수한 성능을 보였다. 이러한 성능 차이는 플럭스 타워 관측범위를 일치시킬 때, 연구 대상지 간의 NIRvP-식물 광합성 관계의 기울기가 일관성을 보였기 때문이다. 본 연구 결과는 위성 관측을 플럭스 타워 관측범위와 일치시키는 것의 중요성을 보여주고 높은 시공간 해상도로 식물 광합성을 원격으로 모니터링하는 초소형위성군 자료의 잠재력을 보여준다.Monitoring changes in terrestrial vegetation is essential to understanding interactions between atmosphere and biosphere, especially terrestrial ecosystem. To this end, satellite remote sensing offer maps for examining land surface in different scales. However, the detailed information was hindered under the clouds or limited by the spatial resolution of satellite imagery. Moreover, the impacts of spatial and temporal resolution in photosynthesis monitoring were not fully revealed.
In this dissertation, I aimed to enhance the spatial and temporal resolution of satellite imagery towards daily gap-free vegetation maps with high spatial resolution. In order to expand vegetation change monitoring in time and space using high-resolution satellite images, I 1) improved temporal resolution of satellite dataset through image fusion using geostationary satellites, 2) improved spatial resolution of satellite dataset using generative adversarial networks, and 3) showed the use of high spatiotemporal resolution maps for monitoring plant photosynthesis especially over heterogeneous landscapes. With the advent of new techniques in satellite remote sensing, current and past datasets can be fully utilized for monitoring vegetation changes in the respect of spatial and temporal resolution.
In Chapter 2, I developed the integrated system that implemented geostationary satellite products in the spatiotemporal image fusion method for monitoring canopy photosynthesis. The integrated system contains the series of process (i.e., cloud masking, nadir bidirectional reflectance function adjustment, spatial registration, spatiotemporal image fusion, spatial gap-filling, temporal-gap-filling). I conducted the evaluation of the integrated system over heterogeneous rice paddy landscape where the drastic land cover changes were caused by cultivation management and deciduous forest where consecutive changes occurred in time. The results showed that the integrated system well predict in situ measurements without data gaps (R2 = 0.71, relative bias = 5.64% at rice paddy site; R2 = 0.79, relative bias = -13.8% at deciduous forest site). The integrated system gradually improved the spatiotemporal resolution of vegetation maps, reducing the underestimation of in situ measurements, especially during peak growing season. Since the integrated system generates daily canopy photosynthesis maps for monitoring dynamics among regions of interest worldwide with high spatial resolution. I anticipate future efforts to reveal the hindered information by the limited spatial and temporal resolution of satellite imagery.
Detailed spatial representations of terrestrial vegetation are essential for precision agricultural applications and the monitoring of land cover changes in heterogeneous landscapes. The advent of satellite-based remote sensing has facilitated daily observations of the Earths surface with high spatial resolution. In particular, a data fusion product such as Planet Fusion has realized the delivery of daily, gap-free surface reflectance data with 3-m pixel resolution through full utilization of relatively recent (i.e., 2018-) CubeSat constellation data. However, the spatial resolution of past satellite sensors (i.e., 30–60 m for Landsat) has restricted the detailed spatial analysis of past changes in vegetation. In Chapter 3, to overcome the spatial resolution constraint of Landsat data for long-term vegetation monitoring, we propose a dual remote-sensing super-resolution generative adversarial network (dual RSS-GAN) combining Planet Fusion and Landsat 8 data to simulate spatially enhanced long-term time-series of the normalized difference vegetation index (NDVI) and near-infrared reflectance from vegetation (NIRv). We evaluated the performance of the dual RSS-GAN against in situ tower-based continuous measurements (up to 8 years) and remotely piloted aerial system-based maps of cropland and deciduous forest in the Republic of Korea. The dual RSS-GAN enhanced spatial representations in Landsat 8 images and captured seasonal variation in vegetation indices (R2 > 0.95, for the dual RSS-GAN maps vs. in situ data from all sites). Overall, the dual RSS-GAN reduced Landsat 8 vegetation index underestimations compared with in situ measurements; relative bias values of NDVI ranged from −3.2% to 1.2% and −12.4% to −3.7% for the dual RSS-GAN and Landsat 8, respectively. This improvement was caused by spatial enhancement through the dual RSS-GAN, which captured fine-scale information from Planet Fusion. This study presents a new approach for the restoration of hidden sub-pixel spatial information in Landsat images.
Mapping canopy photosynthesis in both high spatial and temporal resolution is essential for carbon cycle monitoring in heterogeneous areas. However, well established satellites in sun-synchronous orbits such as Sentinel-2, Landsat and MODIS can only provide either high spatial or high temporal resolution but not both. Recently established CubeSat satellite constellations have created an opportunity to overcome this resolution trade-off. In particular, Planet Fusion allows full utilization of the CubeSat data resolution and coverage while maintaining high radiometric quality. In Chapter 4, I used the Planet Fusion surface reflectance product to calculate daily, 3-m resolution, gap-free maps of the near-infrared radiation reflected from vegetation (NIRvP). I then evaluated the performance of these NIRvP maps for estimating canopy photosynthesis by comparing with data from a flux tower network in Sacramento-San Joaquin Delta, California, USA. Overall, NIRvP maps captured temporal variations in canopy photosynthesis of individual sites, despite changes in water extent in the wetlands and frequent mowing in the crop fields. When combining data from all sites, however, I found that robust agreement between NIRvP maps and canopy photosynthesis could only be achieved when matching NIRvP maps to the flux tower footprints. In this case of matched footprints, NIRvP maps showed considerably better performance than in situ NIRvP in estimating canopy photosynthesis both for daily sum and data around the time of satellite overpass (R2 = 0.78 vs. 0.60, for maps vs. in situ for the satellite overpass time case). This difference in performance was mostly due to the higher degree of consistency in slopes of NIRvP-canopy photosynthesis relationships across the study sites for flux tower footprint-matched maps. Our results show the importance of matching satellite observations to the flux tower footprint and demonstrate the potential of CubeSat constellation imagery to monitor canopy photosynthesis remotely at high spatio-temporal resolution.Chapter 1. Introduction 2
1. Background 2
1.1 Daily gap-free surface reflectance using geostationary satellite products 2
1.2 Monitoring past vegetation changes with high-spatial-resolution 3
1.3 High spatiotemporal resolution vegetation photosynthesis maps 4
2. Purpose of Research 4
Chapter 2. Generating daily gap-filled BRDF adjusted surface reflectance product at 10 m resolution using geostationary satellite product for monitoring daily canopy photosynthesis 6
1. Introduction 6
2. Methods 11
2.1 Study sites 11
2.2 In situ measurements 13
2.3 Satellite products 14
2.4 Integrated system 17
2.5 Canopy photosynthesis 21
2.6 Evaluation 23
3. Results and discussion 24
3.1 Comparison of STIF NDVI and NIRv with in situ NDVI and NIRv 24
3.2 Comparison of STIF NIRvP with in situ NIRvP 28
4. Conclusion 31
Chapter 3. Super-resolution of historic Landsat imagery using a dual Generative Adversarial Network (GAN) model with CubeSat constellation imagery for monitoring vegetation changes 32
1. Introduction 32
2. Methods 38
2.1 Real-ESRGAN model 38
2.2 Study sites 40
2.3 In situ measurements 42
2.4 Vegetation index 44
2.5 Satellite data 45
2.6 Planet Fusion 48
2.7 Dual RSS-GAN via fine-tuned Real-ESRGAN 49
2.8 Evaluation 54
3. Results 57
3.1 Comparison of NDVI and NIRv maps from Planet Fusion, Sentinel 2 NBAR, and Landsat 8 NBAR data with in situ NDVI and NIRv 57
3.2 Comparison of dual RSS-SRGAN model results with Landsat 8 NDVI and NIRv 60
3.3 Comparison of dual RSS-GAN model results with respect to in situ time-series NDVI and NIRv 63
3.4 Comparison of the dual RSS-GAN model with NDVI and NIRv maps derived from RPAS 66
4. Discussion 70
4.1 Monitoring changes in terrestrial vegetation using the dual RSS-GAN model 70
4.2 CubeSat data in the dual RSS-GAN model 72
4.3 Perspectives and limitations 73
5. Conclusion 78
Appendices 79
Supplementary material 82
Chapter 4. Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates 85
1. Introduction 85
2. Methods 89
2.1 Study sites 89
2.2 In situ measurements 92
2.3 Planet Fusion NIRvP 94
2.4 Flux footprint model 98
2.5 Evaluation 98
3. Results 105
3.1 Comparison of Planet Fusion NIRv and NIRvP with in situ NIRv and NIRvP 105
3.2 Comparison of instantaneous Planet Fusion NIRv and NIRvP with against tower GPP estimates 108
3.3 Daily GPP estimation from Planet Fusion -derived NIRvP 114
4. Discussion 118
4.1 Flux tower footprint matching and effects of spatial and temporal resolution on GPP estimation 118
4.2 Roles of radiation component in GPP mapping 123
4.3 Limitations and perspectives 126
5. Conclusion 133
Appendix 135
Supplementary Materials 144
Chapter 5. Conclusion 153
Bibliography 155
Abstract in Korea 199
Acknowledgements 202박
VerSA: Versatile Systolic Array Architecture for Sparse and Dense Matrix Multiplications
No full textA key part of modern deep neural network (DNN) applications is matrix multiplication. As DNN applications are becoming more diverse, there is a need for both dense and sparse matrix multiplications to be accelerated by hardware. However, most hardware accelerators are designed to accelerate either dense or sparse matrix multiplication. In this paper, we propose VerSA, a versatile systolic array architecture for both dense and sparse matrix multiplications. VerSA employs intermediate paths and SRAM buffers between the rows of the systolic array (SA), thereby enabling an early termination in sparse matrix multiplication with a negligible performance overhead when running dense matrix multiplication. When running sparse matrix multiplication, 256 × 256 VerSA brings performance (i.e., an inverse of execution time) improvement and energy saving by 1.21×–1.60× and 7.5–30.2%, respectively, when compared to the conventional SA. When running dense matrix multiplication, VerSA results in only a 0.52% performance overhead compared to the conventional SA
Wrinkled silica/titania nanoparticles with tunable interwrinkle distances for efficient utilization of photons in dye-sensitized solar cells
No full textEfficient light harvesting is essential for the realization of high energy conversion efficiency in dye-sensitized solar cells (DSCs). State-of-the-art mesoporous TiO2 photoanodes fall short for collection of long-wavelength visible light photons, and thus there have been efforts on introduction of scattering nanoparticles. Herein, we report the synthesis of wrinkled silica/titania nanoparticles with tunable interwrinkle distances as scattering materials for enhanced light harvesting in DSCs. These particles with more than 20 times larger specific surface area (>400 m2/g) compared to the spherical scattering particles (<20 m2/g) of the similar sizes gave rise to the dye-loading amounts, causing significant improvements in photocurrent density and efficiency. Moreover, dependence of spectral scattering properties of wrinkled particles on interwrinkle distances, which was originated from difference in overall refractive indices, was observed. © The Author(s) 20161571sciescopu
Pressurized cryogenic air energy storage for efficiency improvement of liquid air energy storage
No full textWith the development of renewable energy sources, large-scale energy storage has been studied such as advanced compressed air energy storage (CAES) and liquid air energy storage (LAES). In this study, a novel pressurized cryogenic air energy storage system (PCAES) is proposed and analyzed. The conventional LAES system produces and stores the liquid air at the ambient pressure. The system achieves 40% to 60% of round-trip efficiency depending on the use of liquid turbo-expander. Meanwhile, this proposed system stores the air near the critical point by expanding it at 40 bar using turbo-expander. This significantly reduces the energy input in comparison to liquefying the air. This system is modeled with commercial process simulation software, Aspen HYSYS v.8.8. It significantly improves the round-trip efficiency of the conventional stand-alone liquid air energy storage system. However, this system requires pressurized tanks for the supercritical air storage. The economic evaluation for energy storage cost should be analyzed depending on variables such as storage time, storage to generation power ratio, and size of the power system. The above-ground CAES, LAES, and the proposed system would be the competitive system without geological limitations and are potentially applicable for the various energy demand-supply environments and the markets
Development of functional living materials for monoclonal antibody purification
No full textFunctional Living Materials (FLMs) offer a promising approach to biological engineering, utilizing living organisms to enhance the quality of products or functional materials. Their key advantages lie in their sustainability and low cost of maintenance. Unlike chemicals, inorganic materials, or even proteins that require constant supply and purchase, FLMs can self-proliferate under specific nutrient conditions, providing a convenient and cost-effective solution. One application explored in this thesis is the development of living materials to replace the existing monoclonal antibody purification process, a crucial step in preparing purified monoclonal antibodies. While current affinity chromatography purification methods are widely used, they account for approximately 30% of the overall cost of monoclonal antibody purification, placing a significant burden on customers who require these antibodies for industrial, research, and medical purposes [1]. To address this challenge, protein A from Staphylococcus aureus was displayed on the surface of Saccharomyces cerevisiae and tested for its efficiency in capturing and eluting monoclonal antibodies. This process mimics the current industry standard using protein A resins. The results demonstrated that this living material effectively captures monoclonal antibodies of interest across a wide range of concentrations and recovers them with increased purity, even in the presence of contaminants. This breakthrough opens up the opportunity to purify monoclonal antibodies on a large scale with significantly reduced costs, eliminating the need for complex and expensive machinery.</p
A Novel Approach for Efficient Gaussian Mixture Model using Dynamics-motivated Optimal Excitation
No full textEffiDynaMix, a novel, efficient, gray-box, nonparametric dynamics modeling method, integrates mathematical dynamics with Gaussian Mixture Model (GMM) for simplified training data creation and improved generalization. It outperforms traditional methods like conv-GMM, GP, and LSTM in training efficiency and accuracy with new data. By leveraging dynamic equations, EffiDynaMix enhances learning efficiency and adaptability, offering advancements in robotic system precision and computational efficiency, leading to faster and more responsive robots. © 2024 IEEE
Evaluation of spatial and temporal variability in Sentinel-2 surface reflectance on a rice paddy landscape
No full textHigh spatial resolution spaceborne remote sensing systems provide a new data source for agricultural applications. As a key deliverable, surface reflectance (SR) enables immediate and non-destructive estimation of crop status, thus the demand for reliable pixelwise SR is increasing. However, the evaluations are typically conducted on pseudo-invariant areas and the reliability of pixelwise SR has not been thoroughly examined over heterogenous, dynamic surfaces. In this study, we evaluated pixelwise Sentinel-2 (S2) SR on a rice paddy landscape across seasons using drone-based hyperspectral images and tower-based continuous hyperspectral observations as the ground references. We also examined the impact of spatial and atmospheric properties on S2 SR. Overall, S2 SR showed strong linear relationships with the ground references (the overall R2 > 0.8). The residual errors were influenced by sub-pixel geolocation errors (0.01–0.017 (2.1–11.8 %)), a widen PSF (0.007 (7.6 %) for red) and underestimated AOT retrievals (0.027 (40.7 %) for blue). Notably, atmospheric adjacency effects broadened the PSF, causing the consistency of S2 with the ground reference image to depend on the landscape's heterogeneity. Our findings outlined the key factors contributing to uncertainties in S2 SR, which could affect downstream products like vegetation indices and leaf area index. Considering these factors would enhance remote sensing of landscapes with high contrast in reflectance and elevated aerosol loadings, such as urban, savanna, wetland and dry agricultural land.Y
