Effects of the Anti-Tumorigenic Agent AT101 on Human Glioblastoma Cells in the Microenvironmental Glioma Stem Cell Niche

Glioblastoma (GBM) is a barely treatable disease due to its profound chemoresistance. A distinct inter- and intratumoral heterogeneity reflected by specialized microenvironmental niches and different tumor cell subpopulations allows GBMs to evade therapy regimens. Thus, there is an urgent need to develop alternative treatment strategies. A promising candidate for the treatment of GBMs is AT101, the R(-) enantiomer of gossypol. The present study evaluates the effects of AT101, alone or in combination with temozolomide (TMZ), in a microenvironmental glioma stem cell niche model of two GBM cell lines (U251MG and U87MG). AT101 was found to induce strong cytotoxic effects on U251MG and U87MG stem-like cells in comparison to the respective native cells. Moreover, a higher sensitivity against treatment with AT101 was observed upon incubation of native cells with a stem-like cell-conditioned medium. This higher sensitivity was reflected by a specific inhibitory influence on the p-p42/44 signaling pathway. Further, the expression of CXCR7 and the interleukin-6 receptor was significantly regulated upon these stimulatory conditions. Since tumor stem-like cells are known to mediate the development of tumor recurrences and were observed to strongly respond to the AT101 treatment, this might represent a promising approach to prevent the development of GBM recurrences.     

自适应融合层级特征的混合退化图像复原算法

多种退化类型混合的图像比单一类型的退化图像降质更严重,很难建立精确模型对其复原,研究端到端的神经网络算法是复原的关键.现有的基于操作选择注意力网络的算法(operation-wiseattentionnetwork,OWAN)虽然有一定的性能提升,但是其网络过于复杂,运行较慢,复原图像缺乏高频细节,整体效果也有提升的空间.针对这些问题,提出一种基于层级特征融合的自适应复原算法.该算法直接融合不同感受野分支的特征,增强复原图像的结构;用注意力机制对不同层级的特征进行动态融合,增加模型的自适应性,降低了模型冗余;另外,结合L1损失和感知损失,增强了复原图像的视觉感知效果.在DIV2K,BSD500等数据集上的实验结果表明,该算法无论是在峰值信噪比和结构相似性上的定量分析,还是在主观视觉质量方面,均优于OWAN算法,充分证明了该算法的有效性.     

YOLSO: You Only Look Small Object

Small object detection is challenging and far from satisfactory. Most general object detectors suffer from two critical issues with small objects: (1) Feature extractor based on classification network cannot express the characteristics of small objects reasonably due to insufficient appearance information of targets and a large amount of background interference around them. (2) The detector requires a much higher location accuracy for small objects than for general objects. This paper proposes an effective and efficient small object detector YOLSO to address the above problems. For feature representation, we analyze the drawbacks in previous backbones and present a Half-Space Shortcut(HSSC) module to build a background-aware backbone. Furthermore, a coarse-to-fine Feature Pyramid Enhancement(FPE) module is introduced for layer-wise aggregation at a granular level to enhance the semantic discriminability. For loss function, we propose an exponential L1 loss to promote the convergence of regression, and a focal IOU loss to focus on prime samples with high classification confidence and high IOU. Both of them significantly improves the location accuracy of small objects. The proposed YOLSO sets state-of-the-art results on two typical small object datasets, MOCOD and VeDAI, at a speed of over 200 FPS. In the meantime, it also outperforms the baseline YOLOv3 by a wide margin on the common COCO dataset.     

面向分拣机器人的珍珠形状视觉检测方法

针对人工进行珍珠形状分拣效率低、精度不稳定等问题,提出基于机器视觉的珍珠形状检测方法;采用背光成像方式消除珍珠表面纹理和光泽的影响,对获取的珍珠图像进行同态滤波等预处理算法,提高图像对比度;为了解决相互接触珍珠影响珍珠轮廓提取的问题,采用分水岭算法对珍珠图像进行分割,得到了独立存在的珍珠个体,再通过连通域标记、质心算法对珍珠进行定位;根据国家标准对珍珠形状的规定,基于珍珠图像信息建立珍珠形状参数模型,对珍珠形状进行量化;实验结果表明,不同形状的珍珠样本的检测误差为0.63%,形状统计精度为100%,算法耗时24 ms;该方法可准确高效地对珍珠进行分拣分级,具有一定的实用价值。     

Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Prediction of mode I fracture toughness (K_IC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression (LMR) and gene expression programming (GEP) methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and elastic modulus (E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets. Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of K_IC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) were utilized herein. In the case of testing datasets, the values of R², RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156, respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R² value and lower errors.     

Ni activated Mo2C nanoparticles supported on stereotaxically-constructed graphene for efficient overall water splitting

Exploring cost-effective, high-efficiency and stable electrocatalysts for overall water splitting is greatly desirable and challenging for sustainable energy. Herein, a novel designed Ni activated molybdenum carbide nanoparticle loaded on stereotaxically-constructed graphene (SCG) using two steps facile strategy (hydrothermal and carbonization) as a bifunctional electrocatalyst for overall water splitting. The optimized Ni/Mo₂C(1:20)-SCG composites exhibit excellent performance with a low overpotential of 150 mV and 330 mV for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), respectively to obtain a current density of 10 mA cm^?2 in 1.0 M KOH solution. In addition, when the optimized Ni/Mo₂C(1:20)-SCG composite is used as a bifunctional electrode for overall water splitting, the electrochemical cell required a low cell voltage of 1.68 V at a current density of 10 mA cm^?2 and long-term stability of 24 h. More significantly, the synergetic effects between Ni-activated Mo₂C nanoparticles and SCG are regarded as a significant contributor to accelerate charge transfer and promote electrocatalytic performance in hybrid electrocatalysts. Our works introduce a novel approach to design advanced bifunctional electrodes for overall water splitting.     

Extracellular Vesicles and Asthma—More Than Just a Co-Existence

Extracellular vesicles (EVs) are membranous structures, which are secreted by almost every cell type analyzed so far. In addition to their importance for cell-cell communication under physiological conditions, EVs are also released during pathogenesis and mechanistically contribute to this process. Here we summarize their functional relevance in asthma, one of the most common chronic non-communicable diseases. Asthma is a complex persistent inflammatory disorder of the airways characterized by reversible airflow obstruction and, from a long-term perspective, airway remodeling. Overall, mechanistic studies summarized here indicate the importance of different subtypes of EVs and their variable cargoes in the functioning of the pathways underlying asthma, and show some interesting potential for the development of future therapeutic interventions. Association studies in turn demonstrate a good diagnostic potential of EVs in asthma.     

Systematic Review: Adipose-Derived Mesenchymal Stem Cells,Platelet-Rich Plasma and Biomaterials as New Regenerative Strategies in Chronic Skin Wounds and Soft Tissue Defects

The number of clinical trials evaluating adipose-derived mesenchymal stem cells (AD-MSCs), platelet-rich plasma (PRP), and biomaterials efficacy in regenerative plastic surgery has exponentially increased during the last ten years. AD-MSCs are easily accessible from various fat depots and show intrinsic plasticity in giving rise to cell types involved in wound healing and angiogenesis. AD-MSCs have been used in the treatment of soft tissue defects and chronic wounds, employed in conjunction with a fat grafting technique or with dermal substitute scaffolds and platelet-rich plasma. In this systematic review, an overview of the current knowledge on this topic has been provided, based on existing studies and the authors’ experience. A multistep search of the PubMed, MEDLINE, Embase, PreMEDLINE, Ebase, CINAHL, PsycINFO, Clinicaltrials.gov, Scopus database, and Cochrane databases has been performed to identify papers on AD-MSCs, PRP, and biomaterials used in soft tissue defects and chronic wounds. Of the 2136 articles initially identified, 422 articles focusing on regenerative strategies in wound healing were selected and, consequently, only 278 articles apparently related to AD-MSC, PRP, and biomaterials were initially assessed for eligibility. Of these, 85 articles were excluded as pre-clinical, experimental, and in vitro studies. For the above-mentioned reasons, 193 articles were selected; of this amount, 121 letters, expert opinions, commentary, and editorials were removed. The remaining 72 articles, strictly regarding the use of AD-MSCs, PRP, and biomaterials in chronic skin wounds and soft tissue defects, were analyzed. The studies included had to match predetermined criteria according to the patients, intervention, comparator, outcomes, and study design (PICOS) approach. The information analyzed highlights the safety and efficacy of AD-MSCs, PRP, and biomaterials on soft tissue defects and chronic wounds, without major side effects.     

Preparation of magnetic zeolite/chitosan composite using silane as modifier for adsorption of Cr(VI) from aqueous solutions

This study aimed to prepare an efficient, cost-effective, and separable magnetic zeolite/chitosan composite (MZFA/CS) adsorbent from solid waste to deal with the water pollution of Cr(VI). The MZFA/CS was characterized by X-ray fluorescence (XRF), Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), and vibrating sample magnetometer (VSM) techniques. Then, the effect of pH, temperature, initial concentration of Cr(VI) ions, and contact time was considered in the study. For a sorbent dose of 0.1 g in 50 mL of a Cr(VI) solution, at a contact time of 30 min, temperature of 30°C, and a pH of 3, an adsorption capacity (q_e) of 16.96 mg g⁻¹ was achieved. Adsorption kinetics and isotherm data obtained for all adsorption systems were well-fitted by pseudo-second-order and Langmuir models, respectively. The thermodynamic study suggested that the adsorption process is spontaneous and endothermic in nature. In summary, the adsorbent with better separability (Ms = 16.83 emu g⁻¹) and adsorbability was successfully fabricated.