Current development and future challenges in microplastic detection techniques: a bibliometrics-based analysis and review.

Microplastics have been considered a new type of pollutant in the marine environment and have attracted widespread attention worldwide in recent years. Plastic particles with particle size less than 5 mm are usually defined as microplastics. Because of their similar size to plankton, marine organisms easily ingest microplastics and can threaten higher organisms and even human health through the food chain. Most of the current studies have focused on the investigation of the abundance of microplastics in the environment. However, due to the limitations of analytical methods and instruments, the number of microplastics in the environment can easily lead to overestimation or underestimation. Microplastics in each environment have different detection techniques. To investigate the current status, hot spots, and research trends of microplastics detection techniques, this review analyzed the papers related to microplastics detection using bibliometric software CiteSpace and COOC. A total of 696 articles were analyzed, spanning 2012 to 2021. The contributions and cooperation of different countries and institutions in this field have been analyzed in detail. This topic has formed two main important networks of cooperation. International cooperation has been a common pattern in this topic. The various analytical methods of this topic were discussed through keyword and clustering analysis. Among them, fluorescent, FTIR and micro-Raman spectroscopy are commonly used optical techniques for the detection of microplastics. The identification of microplastics can also be achieved by the combination of other techniques such as mass spectrometry/thermal cracking gas chromatography. However, these techniques still have limitations and cannot be applied to all environmental samples. We provide a detailed analysis of the detection of microplastics in different environmental samples and list the challenges that need to be addressed in the future

Influence of Incubation Temperature on 9,10-Anthraquinone-2-Sulfonate (AQS)-Mediated Extracellular Electron Transfer

The electron shuttling process has been recognized as an important microbial respiration process. Because the incubation temperature can influence both the reactivity of electron mediators and cell growth, it may also affect the electron-shuttle-mediated extracellular electron transfer (EET) process. Here, the effect of incubation temperature (22–38°C) was investigated in a bioelectrochemical system (BES) using Shewanella oneidensis MR-1 and 50 μM of 9,10-anthraquinone-2-sulfonate (AQS). We found that current generation increased as the temperature was increased from 22 to 34°C and then decreased sharply at 38°C. The biofilm biomass, as indicated by the total protein extracted from the electrode, increased as the temperature increased from 22 to 34°C and then decreased at 38°C, mirroring the current generation results. These results were further confirmed by increasing the temperature slowly, step-by-step, in a single BES with a constant biofilm biomass, suggesting that the EET rates could be substantially influenced by temperature, even with the same biofilm. The effects of temperature on the AQS bioreduction rate, c-type cytochrome (c-Cyts)-bound-cofactor-mediated EET, the AQS mid-point potential, and the AQS diffusion coefficient were studied. From these results, we were able to conclude that temperature influenced the EET rates by changing the c-Cyts-bound-cofactor-mediated EET process and the AQS bioreduction rate, and that the change in biofilm formation was a dominant factor influencing the overall EET rates. These findings should contribute to the fundamental understanding of EET processes. Moreover, optimization of the operating parameters for current generation will be helpful for the practical application of bioelectrochemical techniques

CXCR2 is essential for cerebral endothelial activation and leukocyte recruitment during neuroinflammation

Chemokines and chemokine receptors cooperate to promote immune cell recruitment to the central nervous system (CNS). In this study, we investigated the roles of CXCR2 and CXCL1 in leukocyte recruitment to the CNS using a murine model of neuroinflammation. Wild-type (WT), CXCL1−/−, and CXCR2−/− mice each received an intracerebroventricular (i.c.v.) injection of lipopolysaccharide (LPS). Esterase staining and intravital microscopy were performed to examine neutrophil recruitment to the brain. To assess endothelial activation in these mice, the expression of adhesion molecules was measured via quantitative real-time polymerase chain reaction (PCR) and Western blotting. To identify the cellular source of functional CXCR2, chimeric mice were generated by transferring bone marrow cells between the WT and CXCR2−/− mice. Expression levels of the chemokines CXCL1, CXCL2, and CXCL5 were significantly increased in the brain following the i.c.v. injection of LPS. CXCR2 or CXCL1 deficiency blocked neutrophil infiltration and leukocyte recruitment in the cerebral microvessels. In the CXCR2−/− and CXCL1−/− mice, the cerebral endothelial expression of adhesion molecules such as P-selectin and VCAM-1 was dramatically reduced. Furthermore, the bone marrow transfer experiments demonstrated that CXCR2 expression on CNS-residing cells is essential for cerebral endothelial activation and leukocyte recruitment. Compared with microglia, cultured astrocytes secreted a much higher level of CXCL1 in vitro. Astrocyte culture conditioned medium significantly increased the expression of VCAM-1 and ICAM-1 in cerebral endothelial cells in a CXCR2-dependent manner. Additionally, CXCR2 messenger RNA (mRNA) expression in cerebral endothelial cells but not in microglia or astrocytes was increased following tumor necrosis factor-α (TNF-α) stimulation. The intravenous injection of the CXCR2 antagonist SB225002 significantly inhibited endothelial activation and leukocyte recruitment to cerebral microvessels. CXCL1 secreted by astrocytes and endothelial CXCR2 play essential roles in cerebral endothelial activation and subsequent leukocyte recruitment during neuroinflammation.https://doi.org/10.1186/s12974-015-0316-

Elevated Foxp3+ double-negative T cells are associated with disease progression during HIV infection

Persistent immune activation, which occurs during the whole course of HIV infection, plays a pivotal role in CD4+ T cells depletion and AIDS progression. Furthermore, immune activation is a key factor that leads to impaired immune reconstitution after long-term effective antiretroviral therapy (ART), and is even responsible for the increased risk of developing non-AIDS co-morbidities. Therefore, it’s imperative to identify an effective intervention targeting HIV-associated immune activation to improve disease management. Double negative T cells (DNT) were reported to provide immunosuppression during HIV infection, but the related mechanisms remained puzzled. Foxp3 endows Tregs with potent suppressive function to maintain immune homeostasis. However, whether DNT cells expressed Foxp3 and the accurate function of these cells urgently needed to be investigated. Here, we found that Foxp3+ DNT cells accumulated in untreated people living with HIV (PLWH) with CD4+ T cell count less than 200 cells/µl. Moreover, the frequency of Foxp3+ DNT cells was negatively correlated with CD4+ T cell count and CD4/CD8 ratio, and positively correlated with immune activation and systemic inflammation in PLWH. Of note, Foxp3+ DNT cells might exert suppressive regulation by increased expression of CD39, CD25, or vigorous proliferation (high levels of GITR and ki67) in ART-naive PLWH. Our study underlined the importance of Foxp3+ DNT cells in the HIV disease progression, and suggest that Foxp3+ DNT may be a potential target for clinical intervention for the control of immune activation during HIV infection

Vision based scene understanding for collision avoidance on roadway

Collision Avoidance Systems (CASs) are attracting a lot of attention as one of the most preferred solutions for advanced driver assistance and autonomous driving. However, scene understanding, which is an essential functionality in CASs, remains a major challenge mainly due to the need for real-time understanding of highly dynamic and complex environment. In this research, a number of robust and low complexity vision based scene understanding techniques for collision avoidance on roadway have been proposed. It has been well recognized in the literature that road surface detection in a dynamic environment is both challenging and computationally intensive. An efficient non-parametric road surface detection algorithm that exploits the depth cue is proposed to overcome the limitations of existing road surface detection methods. Unlike existing methods that attempt to fit the road surface into rigid models, the proposed method results in low computational complexity, mainly due to the reliance on four intrinsic road scene attributes observed under stereo geometry. It has been demonstrated that the proposed method is capable of detecting both planar and non-planar road surfaces. Extensive experimental results using three challenging benchmarks (i.e. enpeda, KITTI stereo/flow, and Daimler) show that the proposed road surface detection algorithm outperforms the baseline algorithms both in terms of detection accuracy (up to 23.12%) and runtime performance (up to 95.00%). Next, robust and low complexity algorithm for computing the ego-vehicle’s motion state is proposed. The proposed method estimates the ego-motion of the vehicle by first employing a novel pruning technique to reduce the computational complexity of the corner feature detection process without compromising on the quality of the extracted corner features. A robust and compute-efficient KLT tracker is proposed to facilitate the generation of the feature correspondences. Finally, an early RANSAC termination condition is introduced to the Gaussian-Newton optimization scheme to achieve rapid convergence of the motion estimation process. Evaluations based on the KITTI odometry benchmark show that the proposed visual odometry method outperforms the baseline algorithms both in terms of accuracy (up to 48.36%) and runtime performance. In addition, the proposed algorithm is placed among the top 15% when evaluated using the well-known KITTI odometry platform. Methods for robust and low complexity stereo-vision based obstacle detection and tracking are proposed. Unlike the works that focus only on the detection of vehicles or pedestrians, the proposed obstacle detection method relies on u-v disparity space to detect all obstacles in the scene. A Space of Interest (SOI) is defined to greatly reduce the search space of obstacles prior to employing adaptive hysteresis thresholding and connected component labeling techniques to segment SOI into sets of obstacles. Method for tracking obstacles across frames is also proposed by constructing a distinctive object appearance model. A number of strategies to further increase the distinctiveness and reduce the computational complexity for constructing the object model are also adopted. Finally, an online multi-object tracking framework is proposed by integrating the obstacle detection and data association modules in a robust way. Evaluations using the KITTI tracking benchmark confirm that the proposed obstacle detection and tracking method outperforms the baseline algorithm in terms of tracking accuracy by up to 51.78%. In addition, compared to the baseline algorithm that achieves about 0.23 frame per second (fps), the proposed method lends well for real-time performance with 20 fps. Finally, an efficient and robust risk assessment framework is proposed by integrating the obstacle detection and tracking, and visual odometry methods proposed in this thesis. The Extended Kalman Filter is customized to enhance the robustness of the predicted trajectory of the obstacles for assessing the collision risk. The robustness of collision prediction has been enhanced by accommodating positioning uncertainty. Evaluations based on the KITTI tracking dataset demonstrate that the proposed method are capable of robust and efficient assessment of the collision risk in diverse traffic scenarios. The proposed vision based scene understanding techniques in this research have paved the way towards realizing a real-time capable collision avoidance system that is both affordable and dependable.DOCTOR OF PHILOSOPHY (SCE

Interferon Gamma in African Trypanosome Infections: Friends or Foes?

African trypanosomes cause fatal infections in both humans and livestock. Interferon gamma (IFN-γ) plays an essential role in resistance to African trypanosomes. However, increasing evidence suggests that IFN-γ, when excessively synthesized, also induces immunopathology, enhancing susceptibility to the infection. Thus, production of IFN-γ must be tightly regulated during infections with African trypanosomes to ensure that a robust immune response is elicited without tissue destruction. Early studies have shown that secretion of IFN-γ is downregulated by interleukin 10 (IL-10). More recently, IL-27 has been identified as a negative regulator of IFN-γ production during African trypanosome infections. In this review, we discuss the current state of our understanding of the role of IFN-γ in African trypanosome infections. We have focused on the cellular source of IFN-γ, its beneficial and detrimental effects, and mechanisms involved in regulation of its production, highlighting some recent advances and offering some perspectives on future directions