ScratchDet: Training Single-Shot Object Detectors from Scratch

Current state-of-the-art object objectors are fine-tuned from the off-the-shelf networks pretrained on large-scale classification dataset ImageNet, which incurs some additional problems: 1) The classification and detection have different degrees of sensitivity to translation, resulting in the learning objective bias; 2) The architecture is limited by the classification network, leading to the inconvenience of modification. To cope with these problems, training detectors from scratch is a feasible solution. However, the detectors trained from scratch generally perform worse than the pretrained ones, even suffer from the convergence issue in training. In this paper, we explore to train object detectors from scratch robustly. By analysing the previous work on optimization landscape, we find that one of the overlooked points in current trained-from-scratch detector is the BatchNorm. Resorting to the stable and predictable gradient brought by BatchNorm, detectors can be trained from scratch stably while keeping the favourable performance independent to the network architecture. Taking this advantage, we are able to explore various types of networks for object detection, without suffering from the poor convergence. By extensive experiments and analyses on downsampling factor, we propose the Root-ResNet backbone network, which makes full use of the information from original images. Our ScratchDet achieves the state-of-the-art accuracy on PASCAL VOC 2007, 2012 and MS COCO among all the train-from-scratch detectors and even performs better than several one-stage pretrained methods. Codes will be made publicly available at https://github.com/KimSoybean/ScratchDet.Comment: CVPR2019 Oral Presentation. Camera Ready Versio

Enhancing the specificity and efficiency of polymerase chain reaction using polyethyleneimine-based derivatives and hybrid nanocomposites

There is a general necessity to improve the specificity and efficiency of the polymerase chain reaction (PCR), and exploring the PCR-enhancing mechanism still remains a great challenge. In this paper we report the use of branched polyethyleneimine (PEI)-based derivatives and hybrid nanocomposites as a novel class of enhancers to improve the specificity and efficiency of a nonspecific PCR system. We show that the surface-charge polarity of PEI and PEI derivatives plays a major role in their effectiveness to enhance the PCR. Positively charged amine-terminated pristine PEI, partially (50%) acetylated PEI (PEI-Ac50), and completely acetylated PEI (PEI-Ac) are able to improve PCR efficiency and specificity with an optimum concentration order of PEI < PEI-Ac50 < PEI-Ac, whereas negatively charged carboxyl-terminated PEI (PEI-SAH; SAH denotes succinamic acid groups) and neutralized PEI modified with both polyethylene glycol (PEG) and acetyl (Ac) groups (PEI-PEG-Ac) are unable to improve PCR specificity and efficiency even at concentrations three orders of magnitude higher than that of PEI. Our data clearly suggests that the PCR-enhancing effect is primarily based on the interaction between the PCR components and the PEI derivatives, where electrostatic interaction plays a major role in concentrating the PCR components locally on the backbones of the branched PEI. In addition, multiwalled carbon nanotubes modified with PEI and PEI-stabilized gold nanoparticles are also able to improve the PCR specificity and efficiency with an optimum PEI concentration less than that of the PEI alone, indicating that the inorganic component of the nanocomposites may help improve the interaction between PEI and the PCR components. The developed PEI-based derivatives or nanocomposites may be used as efficient additives to enhance other PCR systems for different biomedical applications

miR-638 is a new biomarker for outcome prediction of non-small cell lung cancer patients receiving chemotherapy.

MicroRNAs (miRNAs), a class of small non-coding RNAs, mediate gene expression by either cleaving target mRNAs or inhibiting their translation. They have key roles in the tumorigenesis of several cancers, including non-small cell lung cancer (NSCLC). The aim of this study was to investigate the clinical significance of miR-638 in the evaluation of NSCLC patient prognosis in response to chemotherapy. First, we detected miR-638 expression levels in vitro in the culture supernatants of the NSCLC cell line SPC-A1 treated with cisplatin, as well as the apoptosis rates of SPC-A1. Second, serum miR-638 expression levels were detected in vivo by using nude mice xenograft models bearing SPC-A1 with and without cisplatin treatment. In the clinic, the serum miR-638 levels of 200 cases of NSCLC patients before and after chemotherapy were determined by quantitative real-time PCR, and the associations of clinicopathological features with miR-638 expression patterns after chemotherapy were analyzed. Our data helped in demonstrating that cisplatin induced apoptosis of the SPC-A1 cells in a dose- and time-dependent manner accompanied by increased miR-638 expression levels in the culture supernatants. In vivo data further revealed that cisplatin induced miR-638 upregulation in the serum derived from mice xenograft models, and in NSCLC patient sera, miR-638 expression patterns after chemotherapy significantly correlated with lymph node metastasis. Moreover, survival analyses revealed that patients who had increased miR-638 levels after chemotherapy showed significantly longer survival time than those who had decreased miR-638 levels. Our findings suggest that serum miR-638 levels are associated with the survival of NSCLC patients and may be considered a potential independent predictor for NSCLC prognosis