Detection and Genetic Analysis of Porcine Bocavirus

Porcine Bocavirus (PBoV) has been reported to be associated with postweaning multisystemic wasting syndrome and pneumonia in pigs. In this study, a survey was conducted to evaluate the prevalence of PBoV in slaughter pigs, sick pigs, asymptomatic pigs and classical swine fever virus (CSFV) eradication plan herds in five provinces of China (Henan, Liaoning, Shandong, Hebei and Tianjin) by means of PCR targeting NS1 gene of PBoV. Among the total of 403 tissue samples, 11.41% were positive for PBoV. The positive rates of spleen (20.75%) and inguinal lymph node (27.18%) are higher than those of other organs. PCR products of twenty PBoV positive samples from slaughter pigs were sequenced for phylogenetic analysis. The result revealed that PBoV could be divided into 6 groups (PBoV-a~PBoV-f). All PBoV sequenced in this study belong to PBoV-a–PBoV-d with 90.1% to 99% nucleotide identities. Our results exhibited significant genetic diversity of PBoV and suggested a complex prevalence of PBoV in Chinese swine herds. Whether this diversity of PBoV has a significance to pig production or even public health remains to be further studied

Enhanced oral bioavailability of cyclosporine A by liposomes containing a bile salt

The main purpose of this study was to evaluate liposomes containing a bile salt, sodium deoxycholate (SDC), as oral drug delivery systems to enhance the oral bioavailability of the poorly water-soluble and poorly permeable drug, cyclosporine A (CyA). Liposomes composed of soybean phosphatidylcholine (SPC) and SDC were prepared by a thin-film dispersion method followed by homogenization. Several properties of the liposomes including particle size, polydispersity index, and entrapment efficiency were characterized. The in vitro release of CyA from these liposomes was less than 5% at 12 hours as measured by a dynamic dialysis method. The pharmacokinetic results in rats showed improved absorption of CyA in SPC/SDC liposomes, compared with CyA-loaded conventional SPC/cholesterol (Chol) liposomes and microemulsion-based Sandimmune Neoral®. The relative oral bioavailability of CyA-loaded SPC/SDC and SPC/Chol liposomes was 120.3% and 98.6%, respectively, with Sandimmun Neoral as the reference. The enhanced bioavailability of CyA was probably due to facilitated absorption by the liposomes containing SDC rather than improved release rate

Multi-Modal Few-Shot Temporal Action Detection via Vision-Language Meta-Adaptation

Few-shot (FS) and zero-shot (ZS) learning are two different approaches for scaling temporal action detection (TAD) to new classes. The former adapts a pretrained vision model to a new task represented by as few as a single video per class, whilst the latter requires no training examples by exploiting a semantic description of the new class. In this work, we introduce a new multi-modality few-shot (MMFS) TAD problem, which can be considered as a marriage of FS-TAD and ZS-TAD by leveraging few-shot support videos and new class names jointly. To tackle this problem, we further introduce a novel MUlti-modality PromPt mETa-learning (MUPPET) method. This is enabled by efficiently bridging pretrained vision and language models whilst maximally reusing already learned capacity. Concretely, we construct multi-modal prompts by mapping support videos into the textual token space of a vision-language model using a meta-learned adapter-equipped visual semantics tokenizer. To tackle large intra-class variation, we further design a query feature regulation scheme. Extensive experiments on ActivityNetv1.3 and THUMOS14 demonstrate that our MUPPET outperforms state-of-the-art alternative methods, often by a large margin. We also show that our MUPPET can be easily extended to tackle the few-shot object detection problem and again achieves the state-of-the-art performance on MS-COCO dataset. The code will be available in https://github.com/sauradip/MUPPETComment: Technical Repor

Pressure induced superconductivity bordering a charge-density-wave state in NbTe4 with strong spinorbit coupling

Transition-metal chalcogenides host various phases of matter, such as charge-density wave (CDW), superconductors, and topological insulators or semimetals. Superconductivity and its competition with CDW in low-dimensional compounds have attracted much interest and stimulated considerable research. Here we report pressure induced superconductivity in a strong spin-orbit (SO) coupled quasi-one-dimensional (1D) transition-metal chalcogenide NbTe$_4$, which is a CDW material under ambient pressure. With increasing pressure, the CDW transition temperature is gradually suppressed, and superconducting transition, which is fingerprinted by a steep resistivity drop, emerges at pressures above 12.4 GPa. Under pressure $p$ = 69 GPa, zero resistance is detected with a transition temperature $T_c$ = 2.2 K and an upper critical field $H_{c2}$= 2 T. We also find large magnetoresistance (MR) up to 102\% at low temperatures, which is a distinct feature differentiating NbTe$_4$ from other conventional CDW materials.Comment: https://rdcu.be/LX8

Serum N‐glycans outperform CA19‐9 in diagnosis of extrahepatic cholangiocarcinoma

Extensive efforts have been devoted to improve the diagnosis of extrahepatic cholangiocarcinoma (ECCA) due to its silent clinical character and lack of effective diagnostic biomarkers. Specific alterations in N‐glycosylation of glycoproteins are considered a key component in cancer progression, which can serve as a distinct molecular signature for cancer detection. This study aims to find potential serum N‐glycan markers for ECCA. In total, 255 serum samples from patients with ECCA (n = 106), benign bile tract disease (BBD, n = 60) and healthy controls (HC, n = 89) were recruited. Only 2 μL of serum from individual patients was used in this assay where the N‐glycome of serum glycoproteins was profiled by DNA sequencer‐assisted fluorophore‐assisted capillary electrophoresis (DSA‐FACE) technology. Multi‐parameter models were constructed by combining the N‐glycans and carbohydrate antigen 19‐9 (CA19‐9) which is currently used clinically. Quantitative analyses showed that among 13 N‐glycan structures, the bifucosylated triantennary N‐glycan (peak10, NA3F2) presented the best diagnostic performance for distinguishing ECCA from BBD and HC. Two diagnostic models (Glycotest1 and Glycotest2) performed better than single N‐glycan or CA19‐9. Additionally, two N‐glycan structures (peak9, NA3Fb; peak12, NA4Fb) were tightly related to lymph node metastasis in ECCA patients. In conclusion, sera of ECCA showed relatively specific N‐glycome profiling patterns. Serum N‐glycan markers and models are novel, valuable and noninvasive alternatives in ECCA diagnosis and progression monitoring.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139072/1/elps6272.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139072/2/elps6272_am.pd

Survey on Video Object Tracking Algorithms

Video object tracking is an important research content in the field of computer vision, mainly studying the tracking of objects with interest in video streams or image sequences. Video object tracking has been widely used in cameras and surveillance, driverless, precision guidance and other fields. Therefore, a comprehensive review on video object tracking algorithms is of great significance. Firstly, according to different sources of challenges, the challenges faced by video object tracking are classified into two aspects, the objects’ factors and the backgrounds’ factors, and summed up respectively. Secondly, the typical video object tracking algorithms in recent years are classified into correlation filtering video object tracking algorithms and deep learning video object tracking algorithms. And further the correlation filtering video object tracking algorithms are classified into three categories: kernel correlation filtering algorithms, scale adaptive correlation filtering algorithms and multi-feature fusion corre-lation filtering algorithms. The deep learning video object tracking algorithms are classified into two categories: video object tracking algorithms based on siamese network and based on convolutional neural network. This paper analyzes various algorithms from the aspects of research motivation, algorithm ideas, advantages and disadvantages. Then, the widely used datasets and evaluation indicators are introduced. Finally, this paper sums up the research and looks forward to the development trends of video object tracking in the future