Anomalous radiation source detection in urban environments is challenging due to the complex nature of background radiation. When a suspicious area is determined, a radiation survey is usually carried out to search for anomalous radiation sources. To locate the source with high accuracy and in a short time, different survey approaches have been studied such as scanning the area with fixed survey paths and data-driven approaches that update the survey path on the fly with newly acquired measurements. In this work, we propose reinforcement learning as a data-driven approach to conduct radiation detection tasks with no human intervention. A simulated radiation environment is constructed, and a convolutional neural network-based double Q-learning algorithm is built and tested for radiation source detection tasks. Simulation results show that the double Q-learning algorithm can reliably navigate the detector and reduce the searching time by at least 44% compared with traditional uniform search methods and gradient search methods.

Modeling of background radiation for the urban environment plays an important role in homeland security. However, background radiation is difficult to assess due to its spatial-temporal fluctuations caused by the variation in soil composition, building materials, and weather patterns etc. To address the challenge of background radiation modeling, we developed a mobile sensor network to continuously monitor the background radiation; we also proposed a maximum likelihood estimation algorithm to decouple and estimate the backgrounds spatial distribution and temporal fluctuation. Experimental results demonstrated how this background radiation monitoring system accurately recognized high background regions in the experimental area, and successfully captured temporal fluctuation trends of background radiation during rains. Our system provides an efficient solution to model the temporal fluctuation and spatial distribution of background radiation.

In radioactive source surveying protocols, a number of task-inherent features degrade the quality of collected gamma ray spectra, including: limited dwell times, a fluctuating background, a large distance to the source, weak source activity, and the low sensitivity of mobile detectors. Thus, collected gamma ray spectra are expected to be sparse and noise dominated. For extremely sparse spectra, direct background subtraction is infeasible and many background estimation techniques do not apply. In this paper, we present a statistical algorithm for source estimation and anomaly detection under such conditions. We employ a fixed-hyperparameter Gaussian processes regression methodology with a linear innovation sequence scheme in order to quickly update an ongoing source distribution estimate with no prior training required. We have evaluated the effectiveness of this approach for anomaly detection using background spectra collected with a Kromek D3S and simulated source spectrum and hyperparameters defined by detector characteristics and information derived from collected spectra. We attained an area under the ROC curve of 0.902 for identifying sparse source peaks within a sparse gamma ray spectrum and achieved a true positive rate of 93% when selecting the optimum thresholding value derived from the ROC curve.

Clostridioides difficile is one of the most common causes of antibiotic-associated diarrhea in developed countries. As key virulence factors of C. difficile, toxin A (TcdA) and toxin B (TcdB) act by glucosylating and inactivating Rho and Ras family small GTPases in host cells, which leads to actin cytoskeleton disruption, cell rounding, and ultimately cell death. Here we present the co-crystal structure of the glucosyltransferase domain (GTD) of TcdA in complex with its substrate human RhoA at 2.60-angstrom resolution. This structure reveals that TcdA GTD grips RhoA mainly through its switch I and switch II regions, which is complemented by interactions involving RhoA's pre-switch I region. Comprehensive structural comparisons between the TcdA GTD-RhoA complex and the structures of TcdB GTD in complex with Cdc42 and R-Ras reveal both the conserved and divergent features of these two toxins in terms of substrate recognition. Taken together, these findings establish the structural basis for TcdA recognition of small GTPases and advance our understanding of the substrates selectivity of large clostridial toxins.

The stability of ion-conductive membranes, such as perfluorosulfonic-acid (PFSA) membranes, as a solid-electrolyte separator in energy devices is strongly linked to their mechanical properties, the characterization of which presents challenges, especially in the presence of ionic interactions. Ionomer membranes' elastic properties are affected by cations; however, their influence on deformation at small and large strains is relatively unexplored. In this paper, we report the stress-strain response and fracture behavior of Nafion membranes exchanged with various cations examined in three deformation regimes. In the small-strain regime, the Young's modulus is strongly dependent on cation size, due to the reduced mobility and local stiffening of polymer chains. The Young's modulus, yield limit and strain-hardening modulus all increase with monovalent cation size in the order H+ < Li+ < Na+ < K+ < Cs+, but with varying dependence. In the failure regime, however, the break strain and fracture energy of the membrane decrease in the presence of larger cations, which promote deformation instability while decreasing plastic dissipation energy during crack propagation, thereby leading to more brittle fracture. These results not only demonstrate the trade-off between strength and fracture toughness, but also reveal how it is altered by the ionic interactions, which also dictate the inverse relationship between stretchability and stiffness. Moreover, the measured stress-strain data are reproduced by the constitutive relations to extract parameters that are correlated to the fracture energy through craze instability. Such relationships provide insight into how parameters extracted from tensile testing can be used to assess membrane stability and the role of ionic interactions.

The incidence of Clostridium difficile infection (CDI) has increased significantly worldwide, causing substantial morbidity and mortality. One of the major virulence factor, TcdB, manages to enter the colonic epithelia via the human frizzled proteins (FZDs), which are physiological receptors for Wnt morphogens. Binding of TcdB to FZDs inhibits Wnt signaling, which may contribute to pathogenesis of CDI. Here, we review the structural mechanism by which TcdB exploits to recognize FZDs for cell entry and inhibiting Wnt signaling, which reveals new strategies to modulate Wnt signaling for therapeutic interventions.

Cancer vaccines have gained widespread attention in recent years as an emerging treatment for tumors. However, most therapeutic cancer vaccines have failed in phase III clinical trials due to faint clinical benefits. In this study, we funded that a specific synbiotic composing Lactobacillus rhamnosus GG (LGG) and jujube powder significantly enhanced the therapeutic effects of whole cells cancer vaccine in MC38 cancer cells bearing-mouse. The utilization of LGG increased the abundance of Muribaculaceae, which is conductive to an enhanced anti-tumor effect, but reduced microbial α-diversity. The use of jujube nursed probiotic microorganisms in Lachnospiaceae and enriched microbial diversity, as indicated by increased Shannon and Chao index. The reshaped gut microbiota by this synbiotic improved lipid metabolism conductive to intensified infiltration of CD8⁺ T cells in the tumor microenvironment and enhanced the potency of above-mentioned cancer vaccine. These encouraging findings are helpful for further efforts towards enhancing the therapeutic effects of cancer vaccines through nutritional intervention.

Background

While anthocyanins are proven to be effective in inhibiting tumour cell proliferation, the underlying mechanisms remain unclear. This research aims to explore the glycosylation of anthocyanins in the tumour inhibitory effects and the potential mechanism.

Methods

The tumour inhibitory effect on mouse colon cancer cells (MC38) was examined by MTT and flow cytometric analyses. The inhibitory pathway of anthocyanin was explored by assessment of tumour cell mitochondrial membrane potential (MMP), the caspase-3 and caspase-9 activity, as well as the cell energy metabolism in terms of the glucose uptake, the NAD⁺/NADH ratio and the ATP level.

Results

We found that 500 μM bilberry anthocyanins extract (BAE) induced 48.1% mitochondrial damage, activated the downstream caspase cascade to form apoptotic bodies (caspase-3 activity increased by 169%, caspase-9 activity increased by 186%), and inhibited cell proliferation (survival rate: 55.97%, 24 h). In contrast, the same concentration of anthocyanidin (cyanidin) led to marginal mitochondrial damage (only 9.85%) and resulted in little inhibition of MC38 cells (survival rate: 86.84%, 24 h). For cells incubated with 500 μM BAE, reactive oxygen species (ROS) decreased by 53.8%, but the ratio of NAD⁺/NADH increased to 3.67, demonstrating that the mitochondrial damage was induced by blocking energy metabolism. Furthermore, cell energy metabolism is related to glucose uptake since the presence of 200 μM GLUT1 inhibitor substantially enhanced the inhibitory effects of cyanidin-3-O-glucoside (Cy-3-Glu) at 500 μM (survival rate: 51.08%, 24 h).

Conclusions

The study suggested that the glycosides of anthocyanins might handicap glucose transport and inhibit energy metabolism, which, in turn, led to mitochondrial damage and apoptosis of tumour cells.