Rosenthalyates6884

Immunodysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome is caused by mutations in forkhead box P3 (FOXP3), which lead to the loss of function of regulatory T cells (Tregs) and the development of autoimmune manifestations early in life. The selective induction of a Treg program in autologous CD4+ T cells by FOXP3 gene transfer is a promising approach for curing IPEX. We have established a novel in vivo assay of Treg functionality, based on adoptive transfer of these cells into scurfy mice (an animal model of IPEX) and a combination of cyclophosphamide (Cy) conditioning and interleukin-2 (IL-2) treatment. This model highlighted the possibility of rescuing scurfy disease after the latter's onset. By using this in vivo model and an optimized lentiviral vector expressing human Foxp3 and, as a reporter, a truncated form of the low-affinity nerve growth factor receptor (ΔLNGFR), we demonstrated that the adoptive transfer of FOXP3-transduced scurfy CD4+ T cells enabled the long-term rescue of scurfy autoimmune disease. The efficiency was similar to that seen with wild-type Tregs. After in vivo expansion, the converted CD4FOXP3 cells recapitulated the transcriptomic core signature for Tregs. These findings demonstrate that FOXP3 expression converts CD4+ T cells into functional Tregs capable of controlling severe autoimmune disease.Pear is one of the most important economic fruits worldwide. The productivity is often negatively affected by drought disaster, but the effects and adaptive mechanism of pear in response to drought stress has not been well understood at the gene transcription levels. Using Illumina HiSeq 2500, the transcriptome from 'Yulu Xiang' Pear leaves were sequenced and analyzed to evaluate the effects of long-term drought stress on the expression of genes in different biosynthetic pathways. Results showed that long-term drought stress weakened antioxidant systematization and impaired the synthesis of photosynthetic pigment in 'Yulu Xiang' Pear leaves. The reduced light utilization and photosynthetic productivity finally resulted in the inhibited fruit development. The transcriptome survey and expression analysis identified 2,207 differentially expressed genes (DEGs) which were summarized into the 30 main functional categories. DEGs analysis showed that the enzyme genes involved in phenylpropanoid biosynthesis under dro provide more valuable information to analyze the function of drought stress-related genes improving plant drought tolerance.The spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical for virus infection through the engagement of the human ACE2 protein1 and is a major antibody target. Here we show that chronic infection with SARS-CoV-2 leads to viral evolution and reduced sensitivity to neutralizing antibodies in an immunosuppressed individual treated with convalescent plasma, by generating whole-genome ultra-deep sequences for 23 time points that span 101 days and using in vitro techniques to characterize the mutations revealed by sequencing. There was little change in the overall structure of the viral population after two courses of remdesivir during the first 57 days. However, after convalescent plasma therapy, we observed large, dynamic shifts in the viral population, with the emergence of a dominant viral strain that contained a substitution (D796H) in the S2 subunit and a deletion (ΔH69/ΔV70) in the S1 N-terminal domain of the spike protein. As passively transferred serum antibodies diminished, viruses with the escape genotype were reduced in frequency, before returning during a final, unsuccessful course of convalescent plasma treatment. In vitro, the spike double mutant bearing both ΔH69/ΔV70 and D796H conferred modestly decreased sensitivity to convalescent plasma, while maintaining infectivity levels that were similar to the wild-type virus.The spike substitution mutant D796H appeared to be the main contributor to the decreased susceptibility to neutralizing antibodies, but this mutation resulted in an infectivity defect. The spike deletion mutant ΔH69/ΔV70 had a twofold higher level of infectivity than wild-type SARS-CoV-2, possibly compensating for the reduced infectivity of the D796H mutation. These data reveal strong selection on SARS-CoV-2 during convalescent plasma therapy, which is associated with the emergence of viral variants that show evidence of reduced susceptibility to neutralizing antibodies in immunosuppressed individuals.Research in cancer care increasingly focuses on survivorship issues, e.g. managing disease- and treatment-related morbidity and mortality occurring during and after treatment. This necessitates innovative approaches that consider treatment side effects in addition to tumor cure. Current treatment-planning methods rely on constrained iterative optimization of dose distributions as a surrogate for health outcomes. The goal of this study was to develop a generally applicable method to directly optimize projected health outcomes. We developed an outcome-based objective function to guide selection of the number, angle, and relative fluence weight of photon and proton radiotherapy beams in a sample of ten prostate-cancer patients by optimizing the projected health outcome. We tested whether outcome-optimized radiotherapy (OORT) improved the projected longitudinal outcome compared to dose-optimized radiotherapy (DORT) first for a statistically significant majority of patients, then for each individual patient. We assessed whether the results were influenced by the selection of treatment modality, late-risk model, or host factors. The results of this study revealed that OORT was superior to DORT. Namely, OORT maintained or improved the projected health outcome of photon- and proton-therapy treatment plans for all ten patients compared to DORT. Furthermore, the results were qualitatively similar across three treatment modalities, six late-risk models, and 10 patients. The major finding of this work was that it is feasible to directly optimize the longitudinal (i.e. long- and short-term) health outcomes associated with the total (i.e. therapeutic and stray) absorbed dose in all of the tissues (i.e. healthy and diseased) in individual patients. This approach enables consideration of arbitrary treatment factors, host factors, health endpoints, and times of relevance to cancer survivorship. It also provides a simpler, more direct approach to realizing the full beneficial potential of cancer radiotherapy.Objective. Dorsal root ganglia (DRG) are promising sites for recording sensory activity. Current technologies for DRG recording are stiff and typically do not have sufficient site density for high-fidelity neural data techniques.Approach. In acute experiments, we demonstrate single-unit neural recordings in sacral DRG of anesthetized felines using a 4.5µm thick, high-density flexible polyimide microelectrode array with 60 sites and 30-40µm site spacing. We delivered arrays into DRG with ultrananocrystalline diamond shuttles designed for high stiffness affording a smaller footprint. We recorded neural activity during sensory activation, including cutaneous brushing and bladder filling, as well as during electrical stimulation of the pudendal nerve and anal sphincter. We used specialized neural signal analysis software to sort densely packed neural signals.Main results. We successfully delivered arrays in five of six experiments and recorded single-unit sensory activity in four experiments. The median neural signal amplitude was 55μV peak-to-peak and the maximum unique units recorded at one array position was 260, with 157 driven by sensory or electrical stimulation. In one experiment, we used the neural analysis software to track eight sorted single units as the array was retracted ∼500μm.Significance. This study is the first demonstration of ultrathin, flexible, high-density electronics delivered into DRG, with capabilities for recording and tracking sensory information that are a significant improvement over conventional DRG interfaces.We present a robust deep learning-based framework for dose calculations of abdominal tumours in a 1.5 T MRI radiotherapy system. For a set of patient plans, a convolutional neural network is trained on the dose of individual multi-leaf-collimator segments following the DeepDose framework. It can then be used to predict the dose distribution per segment for a set of patient anatomies. The network was trained using data from three anatomical sites of the abdomen prostate, rectal and oligometastatic tumours. A total of 216 patient fractions were used, previously treated in our clinic with fixed-beam IMRT using the Elekta MR-linac. For the purpose of training, 176 fractions were used with random gantry angles assigned to each segment, while 20 fractions were used for the validation of the network. The ground truth data were calculated with a Monte Carlo dose engine at 1% statistical uncertainty per segment. For a total of 20 independent abdominal test fractions with the clinical angles, the network was able to accurately predict the dose distributions, achieving 99.4% ± 0.6% for the whole plan prediction at the 3%/3 mm gamma test. The average dose difference and standard deviation per segment was 0.3% ± 0.7%. Additional dose prediction on one cervical and one pancreatic case yielded high dose agreement of 99.9% and 99.8% respectively for the 3%/3 mm criterion. Overall, we show that our deep learning-based dose engine calculates highly accurate dose distributions for a variety of abdominal tumour sites treated on the MR-linac, in terms of performance and generality.

Synthetic CT generation is the focus of many studies, however, only models on data with the same dataset were tested. Therefore, how well the trained model will work for data from different hospitals and MR protocols is still unknown. In this study, we addressed the model generalization problem for the MR-to-CT conversion task.

Brain T2 MR and corresponding CT images were collected from one hospital and brain T1-FLAIR, T1-POST MR, and corresponding CT images were collected from another hospital. To investigate the model's generalizability ability, four potential solutions were proposed source model, target model, combined model, and adapted model. All models were trained using the CycleGAN network. The source model was trained with a source domain dataset from scratch and tested with a target domain dataset. The target model was trained with a target domain dataset and tested with a target domain dataset. The combined model was trained with both source domain and target domain datasets, and tested with thmall training datasets of MR images using pre-trained CycleGAN. The quantitative results of the test data, including different scanning protocols and different acquisition centers, indicated the proof of this concept.The optical characteristics of a planar thin film waveguide system composes of air-graphene-LiNbO3 have been investigated. (L)-Dehydroascorbic price Monolayer or bilayer graphene with high quality are characterized by Raman spectroscopy, scanning electron microscope and atom force microscope. The refractivity and reflectivity of air-graphene-LiNbO3 system are measured experimentally and compared with that of LiNbO3 waveguide by the prism coupling method. The reflectivity shows an overall decrease due to the lower transmittance for graphene on LiNbO3 substrate. The refractivity increases significantly at the wavelength of 1540 nm, which may be attributed to the generation of graphene surface plasmons (SPs) excited by infrared radiation. A shaped air-graphene-LiNbO3 waveguide is designed and simulated by Mode Solutions. The distribution of optical field is performed and analyzed. The preparation of proposed air-graphene-LiNbO3 structure incorporates the commonly used chemical vapor deposition and thin film transfer techniques and is compatible with existing optoelectronic integration processes, which can be employed for building various optical integrated devices.