Protection against H1N1 influenza challenge by a DNA vaccine expressing H3/H1 subtype hemagglutinin combined with MHC class II-restricted epitopes

<p>Abstract</p> <p>Background</p> <p>Multiple subtypes of avian influenza viruses have crossed the species barrier to infect humans and have the potential to cause a pandemic. Therefore, new influenza vaccines to prevent the co-existence of multiple subtypes within a host and cross-species transmission of influenza are urgently needed.</p> <p>Methods</p> <p>Here we report a multi-epitope DNA vaccine targeted towards multiple subtypes of the influenza virus. The protective hemagglutinin (HA) antigens from H5/H7/H9 subtypes were screened for MHC II class-restricted epitopes overlapping with predicted B cell epitopes. We then constructed a DNA plasmid vaccine, pV-H3-EHA-H1, based on HA antigens from human influenza H3/H1 subtypes combined with the H5/H7/H9 subtype Th/B epitope box.</p> <p>Results</p> <p>Epitope-specific IFN-γ ELISpot responses were significantly higher in the multi-epitope DNA group than in other vaccine and control groups (<it>P </it>< 0.05). The multi-epitope group significantly enhanced Th2 cell responses as determined by cytokine assays. The survival rate of mice given the multi-epitope vaccine was the highest among the vaccine groups, but it was not significantly different compared to those given single antigen expressing pV-H1HA1 vaccine and dual antigen expressing pV-H3-H1 vaccine (<it>P </it>> 0.05). No measurable virus titers were detected in the lungs of the multi-epitope immunized group. The unique multi-epitope DNA vaccine enhanced virus-specific antibody and cellular immunity as well as conferred complete protection against lethal challenge with A/New Caledonia/20/99 (H1N1) influenza strain in mice.</p> <p>Conclusions</p> <p>This approach may be a promising strategy for developing a universal influenza vaccine to prevent multiple subtypes of influenza virus and to induce long-term protective immune against cross-species transmission.</p

Potent anti-tumor effects of a dual specific oncolytic adenovirus expressing apoptin in vitro and in vivo

<p>Abstract</p> <p>Background</p> <p>Oncolytic virotherapy is an attractive drug platform of cancer gene therapy, but efficacy and specificity are important prerequisites for success of such strategies. Previous studies determined that Apoptin is a p53 independent, bcl-2 insensitive apoptotic protein with the ability to specifically induce apoptosis in tumor cells. Here, we generated a conditional replication-competent adenovirus (CRCA), designated Ad-hTERT-E1a-Apoptin, and investigated the effectiveness of the CRCA a gene therapy agent for further clinical trials.</p> <p>Results</p> <p>The observation that infection with Ad-hTERT-E1a-Apoptin significantly inhibited growth of the melanoma cells, protecting normal human epidermal melanocytes from growth inhibition confirmed cancer cell selective adenoviral replication, growth inhibition, and apoptosis induction of this therapeutic approach. The <it>in vivo </it>assays performed by using C57BL/6 mice containing established primary or metastatic tumors expanded the <it>in vitro </it>studies. When treated with Ad-hTERT-E1a-Apoptin, the subcutaneous primary tumor volume reduction was not only observed in intratumoral injection group but in systemic delivery mice. In the lung metastasis model, Ad-hTERT-E1a-Apoptin effectively suppressed pulmonary metastatic lesions. Furthermore, treatment of primary and metastatic models with Ad-hTERT-E1a-Apoptin increased mice survival.</p> <p>Conclusions</p> <p>These data further reinforce the previously research showing that an adenovirus expressing Apoptin is more effective and advocate the potential applications of Ad-hTERT-E1a-Apoptin in the treatment of neoplastic diseases in future clinical trials.</p

Fabricating a novel HLC-hBMP2 fusion protein for the treatment of bone defects

Treating serious bone trauma with an osteo-inductive agent such as bone morphogenetic proteins (BMPs) has been considered as an optimized option when delivered via a collagen sponge (CS). Previous work has shown that the BMP concentration and release rate from approved CS carriers is difficult to control with precision. Here we presented the fabrication of a recombinant fusion protein from recombinant human-like collagen (HLC) and human BMP-2 (hBMP2). The fusion protein preserved the characteristic of HLC allowing the recombinant protein to be expressed in Yeast (such as Pichia pastoris GS115) and purified rapidly and easily with mass production after methanol induction. It also kept the stable properties of HLC and hBMP2 in the body fluid environment with good biocompatibility and no cytotoxicity. Moreover, the recombinant fusion protein fabricated a vertical through-hole structure with improved mechanical properties, and thus facilitated migration of bone marrow mesenchymal stem cells (MSCs) into the fusion materials. Furthermore, the fusion protein degraded and released hBMP-2 in vivo allowing osteoinductive activity and the enhancement of utilization rate and the precise control of the hBMP2 release. This fusion protein when applied to cranial defects in rats was osteoinductively active and improved bone repairing enhancing the repairing rate 3.5- fold and 4.2- fold when compared to the HLC alone and the control, respectively. There were no visible inflammatory reactions, infections or extrusions around the implantation sites observed. Our data strongly suggests that this novel recombinant fusion protein could be more beneficial in the treatment of bone defects than the simple superposition of the hBMP2/collagen sponge

Lethal activity of BRD4 PROTAC degrader QCA570 against bladder cancer cells

Bladder cancer is the most common malignancy of the urinary system. Efforts to identify innovative and effective therapies for bladder cancer are urgently needed. Recent studies have identified the BRD4 protein as the critical factor in regulation of cell proliferation and apoptosis in bladder cancer, and it shows promising potential for pharmacologic treatment against bladder cancer. In this study, we have evaluated the biological function of QCA570, a novel BET degrader, on multiple bladder cancer cells and explore its underlying mechanisms. QCA570 potently induces degradation of BRD4 protein at nanomolar concentrations, with a DC50 of ∼ 1 nM. It decreases EZH2 and c-MYC levels by transcriptional suppression and protein degradation. Moreover, the degrader significantly induces cell apoptosis and cycle arrest and shows antiproliferation activity against bladder cancer cells. These findings support the potential efficacy of QCA570 on bladder cancer

Attenuation of Vaccinia Tian Tan Strain by Removal of Viral TC7L-TK2L and TA35R Genes

Vaccinia Tian Tan (VTT) was attenuated by deletion of the TC7L-TK2L and TA35R genes to generate MVTT3. The mutant was generated by replacing the open reading frames by a gene encoding enhanced green fluorescent protein (EGFP) flanked by loxP sites. Viruses expressing EGFP were then screened for and purified by serial plaque formation. In a second step the marker EGFP gene was removed by transfecting cells with a plasmid encoding cre recombinase and selecting for viruses that had lost the EGFP phenotype. The MVTT3 mutant was shown to be avirulent and immunogenic. These results support the conclusion that TC7L-TK2L and TA35R deletion mutants can be used as safe viral vectors or as platform for vaccines

SEG-YOLO: Real-Time Instance Segmentation Using YOLOv3 and Fully Convolutional Network

Computer vision technology has been widely applied to augment sports TV broadcast experience.  Some of these broadcasts require accurate fore- ground segmentation on the outdoor sports scene, i.e., segmentation of po- tential sports players and highlight them. Segmentation on a full-HD video (1080p) resolution video stream with a high frame rate leads big challenge on the segmentation model.  In this thesis, a deep learning-based frame- work is proposed for real-time instance segmentation.Many traditional computer vision algorithms for segmentation based on background subtraction techniques, which are affected a lot by light-switch and  targets’  movements.   On  the  other  hand,  most  of  the  modern  deep learning-based frameworks run at a deficient speed that cannot support real-time usage, although they have better robustness against scene changes. The proposed model, SEG-YOLO, is an extension of YOLO(You Only Look Once) version 3, which is one of the state of the art object detection model. The extension part is FCN(Fully Convolution Network), which is used for semantic segmentation. SEG-YOLO aims to overcome both the speed and accuracy problems on the specific outdoor sports scene, while its usage can also be generalized to some extent.SEG-YOLO is an end to end model that consists of two neural networks: (a) YOLOv3, for object detection to generate instance bounding boxes and also for feature maps extraction as the input of phase b; (b) FCN, takes bound- ing boxes and feature maps as input and output segmentation masks of the objects.For instance, segmentation in the specific outdoor sport like golf, the frame- work shows an excellent performance both in speed and accuracy accord- ing to the experiments, and it’s superior to the state-of-the-art model. More- over,  it is proved that it can be used in real-time (30 FPS) broadcast TV with GPU acceleration.  For non-specific scenes of the benchmark COCO dataset, its performance does not exceed the current state-of-the-art withrespect to accuracy, but still has advantages regarding speed.Ökad sändningsupplevelse kräver korrekt instanssegmentering på utom- hussporter, dvs segmentering av potentiella spelspelare. Segmentering på full-HD video (1080p) upplösningsvideo med hög bildhastighet leder till stor utmaning på segmenteringsmodellen. I denna avhandling föreslås en djupinlärningsbaserad ram för realtidssegmentering.Många traditionella datorvisningsmetoder för segmentering baserat på bak- grunds subtraktionsteknik, vilket drabbade mycket av ljusbrytare och mål- rörelser. Å andra sidan går det mesta av det moderna, djupt lärande base- rade ramverket med en mycket låg hastighet som inte kan användas i real- tid. Den föreslagna modellen, SEG-YOLO, är en förlängning av YOLO (You Only Look Once) version 3, som är en av de senaste teknikerna för detek- tering av objekt. Utvidgningsdelen är FCN (Full Convolution Network), som används för semantisk segmentering.SEG-YOLO är en end-end-modell som består av två neurala nätverk: (a) YOLOv3, för objektdetektering för att generera instansbegränsande lådor och  även  för  extraktion  av  funktionskartor  som  inmatning  av  fas  b;  (b) FCN, tar gränser och kartor som inmatnings- och utmatningsmaskar av objekten.För de specifika utomhusscenerna visar ramverket en bra prestanda både på hastighet och noggrannhet jämfört med Mask R-CNN-ramverket. Och det är bevisat att det kan användas på realtidssändningstvätt med GPU-acceleration

SEG-YOLO: Real-Time Instance Segmentation Using YOLOv3 and Fully Convolutional Network

Computer vision technology has been widely applied to augment sports TV broadcast experience.  Some of these broadcasts require accurate fore- ground segmentation on the outdoor sports scene, i.e., segmentation of po- tential sports players and highlight them. Segmentation on a full-HD video (1080p) resolution video stream with a high frame rate leads big challenge on the segmentation model.  In this thesis, a deep learning-based frame- work is proposed for real-time instance segmentation.Many traditional computer vision algorithms for segmentation based on background subtraction techniques, which are affected a lot by light-switch and  targets’  movements.   On  the  other  hand,  most  of  the  modern  deep learning-based frameworks run at a deficient speed that cannot support real-time usage, although they have better robustness against scene changes. The proposed model, SEG-YOLO, is an extension of YOLO(You Only Look Once) version 3, which is one of the state of the art object detection model. The extension part is FCN(Fully Convolution Network), which is used for semantic segmentation. SEG-YOLO aims to overcome both the speed and accuracy problems on the specific outdoor sports scene, while its usage can also be generalized to some extent.SEG-YOLO is an end to end model that consists of two neural networks: (a) YOLOv3, for object detection to generate instance bounding boxes and also for feature maps extraction as the input of phase b; (b) FCN, takes bound- ing boxes and feature maps as input and output segmentation masks of the objects.For instance, segmentation in the specific outdoor sport like golf, the frame- work shows an excellent performance both in speed and accuracy accord- ing to the experiments, and it’s superior to the state-of-the-art model. More- over,  it is proved that it can be used in real-time (30 FPS) broadcast TV with GPU acceleration.  For non-specific scenes of the benchmark COCO dataset, its performance does not exceed the current state-of-the-art withrespect to accuracy, but still has advantages regarding speed.Ökad sändningsupplevelse kräver korrekt instanssegmentering på utom- hussporter, dvs segmentering av potentiella spelspelare. Segmentering på full-HD video (1080p) upplösningsvideo med hög bildhastighet leder till stor utmaning på segmenteringsmodellen. I denna avhandling föreslås en djupinlärningsbaserad ram för realtidssegmentering.Många traditionella datorvisningsmetoder för segmentering baserat på bak- grunds subtraktionsteknik, vilket drabbade mycket av ljusbrytare och mål- rörelser. Å andra sidan går det mesta av det moderna, djupt lärande base- rade ramverket med en mycket låg hastighet som inte kan användas i real- tid. Den föreslagna modellen, SEG-YOLO, är en förlängning av YOLO (You Only Look Once) version 3, som är en av de senaste teknikerna för detek- tering av objekt. Utvidgningsdelen är FCN (Full Convolution Network), som används för semantisk segmentering.SEG-YOLO är en end-end-modell som består av två neurala nätverk: (a) YOLOv3, för objektdetektering för att generera instansbegränsande lådor och  även  för  extraktion  av  funktionskartor  som  inmatning  av  fas  b;  (b) FCN, tar gränser och kartor som inmatnings- och utmatningsmaskar av objekten.För de specifika utomhusscenerna visar ramverket en bra prestanda både på hastighet och noggrannhet jämfört med Mask R-CNN-ramverket. Och det är bevisat att det kan användas på realtidssändningstvätt med GPU-acceleration

Spatiotemporal Variability in Precipitation Extremes in the Jianghuai Region of China and the Analysis of Its Circulation Features

In the context of global warming, changes in extreme-precipitation events are becoming increasingly complex, and investigating the spatial and temporal variation characteristics of extreme precipitation is extremely important for scientific water-resource planning, preventing new climate risks and maintaining ecosystem balances. Based on the daily precipitation from 1960&ndash;2017 at 15 meteorological stations in the Jianghuai region, the extreme-precipitation indices were calculated. The variations in 12 extreme-precipitation indices were detected by using the Mann&ndash;Kendall test in the Jianghuai region. The periodicity of indices was examined by wavelet analysis detecting significant time sections. Through the cross wavelet transform and wavelet coherence analyses, the nonlinear connections between extreme precipitation and atmospheric circulation were explored. The results indicate significant increasing trends in the max one-day precipitation amount (Rx1day), extreme wet days (R99p), and simple precipitation intensity index (SDII). The intensity of extreme precipitation increased significantly. The variation in extreme precipitation showed different trends in different regions, with a greater likelihood of increasing extreme-precipitation intensity and frequency in the southern region compared to the central and northern regions. The period of most oscillations of the indices tend toward be on a time scale of 2&ndash;4 years and are in the 1990s. The number of heavy precipitation days (R10 mm) and number of very heavy precipitation days (R20 mm) had, mainly, periods of 5.84 years. Additionally, there were significant resonance periods between the extreme-precipitation indices and the atmospheric circulation index; however, there were obvious differences in time domains. The North Atlantic Oscillation (NAO) and East Asian summer monsoon (EASM) had the most significant effect on the duration of extreme precipitation; Atlantic Oscillation (AO) and EASM had the most significant influence on the extreme-precipitation intensity. The results of the study can provide a scientific basis for water-resource management and disaster prevention and control in the Jianghuai region

Generalized Inexact Newton-Landweber Iteration for Possibly Non-Smooth Inverse Problems in Banach Spaces

In this paper, we consider a generalized inexact Newton-Landweber iteration to solve nonlinear ill-posed inverse problems in Banach spaces, where the forward operator might not be Gâteaux differentiable. The method is designed with non-smooth convex penalty terms, including L1-like and total variation-like penalty functionals, to capture special features of solutions such as sparsity and piecewise constancy. Furthermore, the inaccurate inner solver is incorporated into the minimization problem in each iteration step. Under some assumptions, based on ε-subdifferential, we establish the convergence analysis of the proposed method. Finally, some numerical simulations are provided to illustrate the effectiveness of the method for solving both smooth and non-smooth nonlinear inverse problems