Total body irradiation (TBI) can result in death associated with hematopoietic insufficiency. Although radiation causes apoptosis of white blood cells, red blood cells (RBC) undergo hemolysis due to hemoglobin denaturation. RBC lysis post-irradiation results in the release of iron into the plasma, producing a secondary toxic event. We investigated radiation-induced iron in the spleens of mice following TBI and the effects of the radiation mitigator captopril. RBC and hematocrit were reduced ~7 days (nadir ~14 days) post-TBI. Prussian blue staining revealed increased splenic Fe3+ and altered expression of iron binding and transport proteins, determined by qPCR, western blotting, and immunohistochemistry. Captopril did not affect iron deposition in the spleen or modulate iron-binding proteins. Caspase-3 was activated after ~7-14 days, indicating apoptosis had occurred. We also identified markers of iron-dependent apoptosis known as ferroptosis. The p21/Waf1 accelerated senescence marker was not upregulated. Macrophage inflammation is an effect of TBI. We investigated the effects of radiation and Fe3+ on the J774A.1 murine macrophage cell line. Radiation induced p21/Waf1 and ferritin, but not caspase-3, after ~24 h. Radiation ± iron upregulated several markers of pro-inflammatory M1 polarization; radiation with iron also upregulated a marker of anti-inflammatory M2 polarization. Our data indicate that following TBI, iron accumulates in the spleen where it regulates iron-binding proteins and triggers apoptosis and possible ferroptosis.
Crystallography is intrinsically limited by its reliance on signal averaged over large collections of perfectly ordered molecules within a single crystal lattice or even across multiple crystals.This is particularly pronounced when crystals contain severe disorder pathologies or are beam sensitive.Recent advances in x-ray and electron diffraction have reduced the minimum size of crystals useful for structure determination to 100s of nms1-3, overcoming many of these difficulties.We previously applied 4-dimensional scanning transmission electron microscopy (4D-STEM), a scanning diffraction technique, to the study of nanomosaicity within a single micron-scale crystal4.This highlighted a potential opportunity for using scanning nano-beam diffraction for structure determination.In our current work, we extend developments in 4D-STEM by incorporating tomography to solve atomic structures of macromolecules from specific regions of polymer nanocrystals.Scanning nanobeam electron diffraction tomography (nanoEDT) consists of scanning a sub-10nm electron probe over a peptide nanocrystal whilst rotating the crystal in discrete, 1-degree intervals5.At each tilt angle, a 4D-STEM dataset is collected by a direct electron detector, capturing thousands of sparse diffraction patterns mapped to specific locations within the crystal.The use of direct electron detection, in combination with cryogenic data collection and a hybrid counting algorithm, allows even weak signals from high-resolution Bragg peaks to be accurately recorded from radiation-sensitive crystals.This intensity data, representing an angular wedge of reciprocal space, is extracted from computationally defined regions of the scan and used to compute structures via fragment-based phasing methods.By scanning the beam, we can collect diffraction from a wide field-of-view and digitally recombine it for later analysis, obviating the need for a selected area aperture.Data collected by nanoEDT compares favourably with data acquired under more conventional electron diffraction methodologies (microED), with minimal evidence of radiation damage.NanoEDT breaks new ground in nanocrystallography by allowing atomic structures to be determined from any region of a nanocrystal through the use of virtual apertures, potentially leading to the determination of atomic structures from heterogeneous or polycrystalline nanoassemblies.References 1. Lanza, A. et al.Nanobeam precession-assisted 3D electron diffraction reveals a new polymorph of hen egg-white lysozyme.IUCrJ 6, (2019).2. Mugnaioli, E. et al.Ab Initio Structure Determination of Cu2-x Te Plasmonic Nanocrystals by Precession-Assisted Electron Diffraction Tomography and HAADF-STEM Imaging.Inorganic chemistry 57,
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A new fourty six 2-(4-(6-chloro-2-benzoxazolyloxy)phenoxy)-N-phenylpro- pionamide derivatives were synthesized and the herbicidal activities against rice plant and barnyard grass with pre-emergence in down land were measured. The structure activity relationships (SAR) between the activities and physicochemical parameters of the substituted(X) N-phenyl group in substrates were analyzed and discussed by Free- Wilson and Hansch method from the basis on the former study (Sung. et. al., 1999). The conditions of selective herbicide activity both the barnyard grass and rice plant are shown that the optimal hydrophobicity, and electron donating with field effect (F, 3g/ha) is selected as the most highest herbicidal activity against barnyard grass in green house.
Background: Performance of deep-learning based automated detection (DLAD) algorithms in systematic screening for active pulmonary tuberculosis is unknown. We aimed to validate DLAD algorithm for detection of active pulmonary tuberculosis and any radiologically-identifiable relevant abnormality on chest radiographs (CRs) in this setting.Methods: We performed out-of-sample testing of a trained DLAD algorithm, using CRs from 19,686 asymptomatic individuals as part of systematic screening for tuberculosis between January 2013 and July 2018. Area under the receiver operating characteristic curves (AUC) of DLAD for diagnosis of tuberculosis and any relevant abnormalities were measured. Accuracy measures including sensitivities, specificities, positive predictive values (PPVs), negative predictive values (NPVs) were calculated at pre-defined operating thresholds (high sensitivity threshold, 0·16; high specificity threshold, 0·46).Findings: All five CRs with active pulmonary tuberculosis were correctly classified as having abnormal findings by DLAD with specificities of 0·959 and 0·997, PPVs of 0·006 and 0·068, and NPVs of both 1·000 at high sensitivity and high specificity thresholds, respectively. With high specificity thresholds, DLAD showed comparable diagnostic measures for tuberculosis to the pooled radiologists (P values > 0·05). For the detection of any radiologically-identifiable relevant abnormality (n=28), DLAD showed AUC value of 0·967 (95% confidence interval, 0·938-0·996) with sensitivities of 0·821 and 0·679, specificities of 0·960 and 0·997, PPVs of 0·028 and 0·257, and NPVs of both 1·000 at high sensitivity and high specificity thresholds, respectively.Interpretation: In systematic screening for tuberculosis in a low-prevalence setting, DLAD algorithm demonstrated excellent diagnostic performance, comparable to the radiologists in the detection of active pulmonary tuberculosis.Funding: Seoul Research & Business Development Program.Declaration of Interest: Researchers (J.H.L., E.J.H., W.Y.L., S.L., J.R.A.) who controlled, manipulated and analyzed data, did not have any conflict of interest. Three authors (J.M.G., H.K., C.M.P.) received research grants from Lunit Inc. for outside of this study.Ethical Approval: This retrospective study was approved by the institutional review board of the Armed Forces Medical Command of Korea, and the requirement for informed consent was waived.
대용량 실험으로부터 산출된 단백질 상호작용 데이터는 위양성(false positive) 데이터의 비율이 높다는 단점을 가지고 있다. 본 논문에서는 오류가 섞여있는 단백질 상호작용 데이터를 입력으로 받아 각 단백질 상호작용의 신뢰도를 검증하는 시스템을 제안하고 구현하였다. 제안 시스템은 단백질 상호작용 데이터에 상호작용의 근거로서 사용될 수 있는 다양한 생물학적 특징들에 관한 데이터를 통합하고 특징 선택 방법을 사용하여 통합된 속성들 중 위양성 여부를 판별하는데 가장 적합한 특징들을 선택한 후 데이터 마이닝 분류 알고리즘을 적용하여 대용량 실험으로부터 산출된 단백질 상호작용 데이터의 신뢰도를 평가한다. 특징 선택의 결과와 분류 기법의 성능은 데이터 특성에 매우 의존하므로, 제안시스템에 가장 적합한 속성 부분집합과 가장 좋은 성능을 내는 분류 알고리즘을 찾기 위해 다양한 특징 선택 방법과 데이터 마이닝 분류 알고리즘들을 적용하고 그 성능을 다각적으로 비교분석 하였다. 실험 결과, 특징 선택 방법과 분류 알고리즘을 결합시킨 제안 시스템은 오류 데이터가 섞여있는 단백질 상호작용 데이터에서 실제로 상호작용하는 단백질 쌍을 골라내는 작업에 있어 기존 연구들에 비해 매우 뛰어난 성능을 보여줬다. 또한 본 연구를 통해 단백질 상호작용 데이터의 신뢰도를 검증함에 있어서 다양한 특징 선택 방법들과 분류 알고리즘들이 성능에 미치는 영향에 관해서도 정리할 수 있었다. Protein-protein interaction data obtained from high-throughput experiments includes high false positives. In this paper, we introduce a new protein-protein interaction reliability verification system. The proposed system integrates various biological features related with protein-protein interactions, and then selects the most relevant and informative features among them using a feature selection method. To assess the reliability of each protein-protein interaction data, the system construct a classifier that can distinguish true interacting protein pairs from noisy protein-protein interaction data based on the selected biological evidences using a classification technique. Since the performance of feature selection methods and classification techniques depends heavily upon characteristics of data, we performed rigorous comparative analysis of various feature selection methods and classification techniques to obtain optimal performance of our system. Experimental results show that the combination of feature selection method and classification algorithms provide very powerful tools in distinguishing true interacting protein pairs from noisy protein-protein interaction dataset. Also, we investigated the effects on performances of feature selection methods and classification techniques in the proposed protein interaction verification system.
In conventional epitaxy of single-crystalline semiconductor materials, it is challenging to separate the grown layer with the substrate due to the strong bonding at the interface. Remote epitaxy is a recently discovered method to grow single-crystalline thin films on graphene, wherein the grown film can be exfoliated at the graphene interface to form freestanding membranes. Here, we present our recent development on remote epitaxy of III-V semiconductors. We show that directly growing 2D materials on III-V substrates as a remote epitaxy template is an ideal pathway that can eliminate transfer process-related defects and can realize wafer-scale process of remote epitaxy and substrate reuse. We present the strategies to grow 2D materials on the surface of III-V materials, which is much more challenging than thermally robust substrates such as SiO2/Si or sapphire. The nucleation of III-V on 2D material-coated III-V platforms via remote interaction is investigated both experimentally and theoretically. Lastly, we show advanced remote epitaxial platforms and optoelectronic applications enabled by remote epitaxy, and the capability to recycle the III-V substrates for repeated remote epitaxy and production of freestanding III-V thin films.
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