OCT1 regulates the migration of colorectal cancer cells by acting on LDHA

Colorectal cancer is one of the most common cancers with high morbidity and mortality. Effective treatments to improve the prognosis are still lacking. The results of online analysis tools showed that OCT1 and LDHA were highly expressed in colorectal cancer, and the high expression of OCT1 was associated with poor prognosis. Immunofluorescence demonstrated that OCT1 and LDHA co-localized in colorectal cancer cells. In colorectal cancer cells, OCT1 and LDHA were upregulated by OCT1 overexpression, but downregulated by OCT1 knockdown. OCT1 overexpression promoted cell migration. OCT1 or LDHA knockdown inhibited the migration, and the downregulation of LDHA restored the promoting effect of OCT1 overexpression. OCT1 upregulation increased the levels of HK2, GLUT1 and LDHA proteins in colorectal cancer cells. Consequently, OCT1 promoted the migration of colorectal cancer cells by upregulating LDHA

A novel semisupervised support vector machine classifier based on active learning and context information

This paper proposes a novel semisupervised support vector machine classifier (Formula presented.) based on active learning (AL) and context information to solve the problem where the number of labeled samples is insufficient. Firstly, a new semisupervised learning method is designed using AL to select unlabeled samples as the semilabled samples, then the context information is exploited to further expand the selected samples and relabel them, along with the labeled samples train (Formula presented.) classifier. Next, a new query function is designed to enhance the reliability of the classification results by using the Euclidean distance between the samples. Finally, in order to enhance the robustness of the proposed algorithm, a fusion method is designed. Several experiments on change detection are performed by considering some real remote sensing images. The results show that the proposed algorithm in comparison with other algorithms can significantly improve the detection accuracy and achieve a fast convergence in addition to verify the effectiveness of the fusion method developed in this paper

Synthesis of porous reduced graphene oxide as metal-free carbon for adsorption and catalytic oxidation of organics in water

Activation of reduced graphene oxide (RGO) using CO2 to obtain highly porous and metal-free carbonaceous materials for adsorption and catalysis was investigated. A facile one-pot thermal process can simultaneously reduce graphene oxide and produce activated RGO without introducing any solid or aqueous activation agent. This process can significantly increase the specific surface area (SSA) of RGO from 200 to higher than 1200 m2 g-1, and the obtained materials were proven to be highly effective for adsorptive removal of both anionic (phenol) and cationic (methylene blue, MB) organics from water. Moreover, the activated RGO materials exhibited much better activity in effective activation of peroxymonosulfate (PMS) to produce sulfate radicals for oxidative degradation of MB

MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition

Malware open-set recognition (MOSR) aims at jointly classifying malware samples from known families and detect the ones from novel unknown families, respectively. Existing works mostly rely on a well-trained classifier considering the predicted probabilities of each known family with a threshold-based detection to achieve the MOSR. However, our observation reveals that the feature distributions of malware samples are extremely similar to each other even between known and unknown families. Thus the obtained classifier may produce overly high probabilities of testing unknown samples toward known families and degrade the model performance. In this paper, we propose the Multi-modal Dual-Embedding Networks, dubbed MDENet, to take advantage of comprehensive malware features (i.e., malware images and malware sentences) from different modalities to enhance the diversity of malware feature space, which is more representative and discriminative for down-stream recognition. Last, to further guarantee the open-set recognition, we dually embed the fused multi-modal representation into one primary space and an associated sub-space, i.e., discriminative and exclusive spaces, with contrastive sampling and rho-bounded enclosing sphere regularizations, which resort to classification and detection, respectively. Moreover, we also enrich our previously proposed large-scaled malware dataset MAL-100 with multi-modal characteristics and contribute an improved version dubbed MAL-100+. Experimental results on the widely used malware dataset Mailing and the proposed MAL-100+ demonstrate the effectiveness of our method.Comment: 14 pages, 7 figure

Transdermal Delivery of Functional Collagen \u3cem\u3eVia\u3c/em\u3e Polyvinylpyrrolidone Microneedles

Collagen makes up a large proportion of the human body, particularly the skin. As the body ages, collagen content decreases, resulting in wrinkled skin and decreased wound healing capabilities. This paper presents a method of delivering type I collagen into porcine and human skin utilizing a polyvinylpyrrolidone microneedle delivery system. The microneedle patches were made with concentrations of 1, 2, 4, and 8% type I collagen (w/w). Microneedle structures and the distribution of collagen were characterized using scanning electron microscopy and confocal microscopy. Patches were then applied on the porcine and human skin, and their effectiveness was examined using fluorescence microscopy. The results illustrate that this microneedle delivery system is effective in delivering collagen I into the epidermis and dermis of porcine and human skin. Since the technique presented in this paper is quick, safe, effective and easy, it can be considered as a new collagen delivery method for cosmetic and therapeutic applications

Robust Respiration Sensing Based on Wi-Fi Beamforming

Currently, the robustness of most Wi-Fi sensing systems is very limited due to that the target’s reflection signal is quite weak and can be easily submerged by the ambient noise. To address this issue, we take advantage of the fact that Wi-Fi devices are commonly equipped with multiple antennas and introduce the beamforming technology to enhance the reflected signal as well as reduce the time-varying noise. We adopt the dynamic signal energy ratio for sub-carrier selection to solve the location dependency problem, based on which a robust respiration sensing system is designed and implemented. Experimental results show that when the distance between the target and the transceiver is 7m,the mean absolute error of the respiration sensing system is less than0.729bpm and the corresponding accuracy reaches 94.79%, which out performs the baseline methods