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Nigella sativa (N. sativa) seed has been an important nutritional flavoring agent and natural remedy for many ailments for centuries in ancient systems of medicine, e.g. Unani, Ayurveda, Chinese and Arabic Medicines. Many active components have been isolated from N. sativa, including thymoquinone, thymohydroquinone, dithymoquinone, thymol, carvacrol, nigellimine-N-oxide, nigellicine, nigellidine and alpha-hederin. In addition, quite a few pharmacological effects of N. sativa seed, its oil, various extracts and active components have been identified to include immune stimulation, anti-inflammation, hypoglycemic, antihypertensive, antiasthmatic, antimicrobial, antiparasitic, antioxidant and anticancer effects. Only a few authors have reviewed the medicinal properties of N. sativa and given some description of the anticancer effects. A literature search has revealed that a lot more studies have been recently carried out related to the anticancer activities of N. sativa and some of its active compounds, such as thymoquinone and alpha-hederin. Acute and chronic toxicity studies have recently confirmed the safety of N. sativa oil and its most abundant active component, thymoquinone, particularly when given orally. The present work is aimed at summarizing the extremely valuable work done by various investigators on the effects of N. sativa seed, its extracts and active principles against cancer. Those related to the underlying mechanism of action, derivatives of thymoquinone, nano thymoquinone and combinations of thymoquinone with the currently used cytotoxic drugs are of particular interest. We hope this review will encourage interested researchers to conduct further preclinical and clinical studies to evaluate the anticancer activities of N. sativa, its active constituents and their derivatives.

Traditional Chinese medicine (TCM) plays a systemic role in disease treatment, targeting multiple etiological factors simultaneously. Based on clinical experience, rhubarb and Salvia miltiorrhiza are commonly prescribed together for the treatment of chronic kidney disease (CKD) and have been proven to be very effective. However, the rationale of the combination remains unclear. The major active ingredients of these two herbs are rhein (RH) and danshensu (DSS), respectively. The aim of this paper is to investigate the renoprotective effects of RH and DSS in vitro and in vivo, and the underlying mechanism. A total of 5/6 nephrectomy rats and HK-2 cells were subjected to chronic renal injury. The combination of RH and DSS conferred a protective effect, as shown by a significant improvement in the renal function, blood supply, and fibrotic degree. Proinflammatory cytokines and adhesion molecules were suppressed by RH and DSS through NK-$\kappa$B signaling. The combination also inhibited apoptosis by up-regulating Bcl-2 and down-regulating Bax. Inhibiting the TGF-$\beta$/Smad3 pathway was at least in part involved in the antifibrotic mechanism of the combination treatment of RH and DSS. This study demonstrates for the first time the renoprotective effect and the mechanism of RH and DSS combination on chronic renal injury. It could provide experimental evidence to support the rationality of the combinatorial use of TCM in clinical practices.

Compared with monotherapy, combination therapy is the first choice and the most promising method for the treatment of many complex diseases. Due to the wide variety of drugs, it is often difficult to choose desirable combination drugs with synergy and low risk. Additional research should always be done before combining drugs because the combinatorial effects can be synergistic, additive, or even antagonistic. Synergistic drugs work together to cause an effect greater than the sum of its parts. Some studies propose different approaches to detect synergism between two or more drugs. Based on the framework of bifurcation-based method, we propose an approach to screen another potential synergistic drug for a given drug. Different from other methods, the approach can help us screen and detect drugs which have a synergistic effect with a known drug, thus playing critical roles in combination therapy. In order to demonstrate the effectiveness of the approach, we apply it to three models, i.e. the zeroth-order reaction model, the galactose model, and the epithelial-to-mesenchymal transition network. The approach provides a theoretical basis for rational design of combination drugs and new use of old drugs.

When several classifiers are brought to contribute to the same task of recognition, various strategies of decisions, implying these classifiers in different ways, are possible. A first strategy consists in deciding using different opinions: it corresponds to the combination of classifiers. A second strategy consists in using one or more opinions for better guiding other classifiers in their training stages, and/or to improve the decision-making of other classifiers in the classification stage: it corresponds to the cooperation of classifiers. The third and last strategy consists in giving more importance to one or more classifiers according to various criteria or situations: it corresponds to the selection of classifiers. The temporal aspect of Pattern Recognition (PR), i.e. the possible evolution of the classes to be recognized, can be treated by the strategy of selection.

Camera can sensor the environment on the lane by extracting the lane lines, but such detection is limited to a short distance with effect of illumination and other factors; radar can detect objects a long distance away but cannot detect the lane conditions. This paper combined machine vision with millimeter-wave radar and extracted the nearby distinct lane line through images; at the same time, the radar obtained the motion trajectory information of distant vehicles, then the least-square method was used to make curve fitting on those motion trajectory information in order to reconstruct the lane line information. Finally, in the stage of fusing two segments of lane lines, the goodness of fit was applied to complete the matching of corresponding lane lines. While, for areas between two segments of lane lines that neither camera or radar can detect, we established a lane model, utilized probabilistic neural network to select the corresponding lane model for matching, and then used approximate mathematics expression according to the selected lane model, thus obtaining the final front road information of current vehicle.

Bayesian networks are powerful tools for common knowledge representation and reasoning of partial beliefs under uncertainty. In the last decade, Bayesian networks have been successfully applied to a variety of problem domains and many Bayesian networks have been established. Confronted with many real-world applications, each Bayesian network established may be a local model of the whole knowledge domain. It is desirable to combine all local models of the whole domain into a global and more general representation. It is not realistic to expect the domain experts to construct the global model manually due to the too broad domain. As well, it is not feasible to relearn the model since the dataset may have been discarded or the whole domain may be distributed. Thus, constructing the global model by combining local models is doomed to be an alternative solution. This paper concentrates on finding a method of combination without loss of any information and free of datasets by capturing the graphical characterizations of global models. From the graphical perspective, this paper first captures two graphical characterizations to determine the skeleton and V-structure of global models. Moreover, a simple algorithm is elicited from these graphical characterizations to recover the global underlying model from multiple local models. A preliminary experiment demonstrates empirically that our algorithm is feasible.

Atanassov (Intuitionistic fuzzy set, Fuzzy Sets and Systems20 (1986) 87–96) proposed the intuitionistic fuzzy set (A-IFS) that is a generalization of Zadeh's fuzzy set. The essential elements of an A-IFS are intuitionistic fuzzy values (IFVs), each of which is depicted by a membership degree and a non-membership degree. The existing literature only gives the explanation of the meaning and the aggregation operators of IFVs, which is very hard and non-visualized for us to comprehend. To overcome the above shortcomings, this paper puts forward the diagram method to analyze the structures of the basic aggregation operations of IFVs. In addition, this paper utilizes the diagram method which is similar to joint probability distribution to describe the components of aggregation operations of IFVs, and each IFV depicts a 3-dimensional vector which is regarded as the marginal distributions of the joint probability distribution.

We study probability intervals as an interesting tool to represent uncertain information. A number of basic operations necessary to develop a calculus with probability intervals, such as combination, marginalization, conditioning and integration are studied in detail. Moreover, probability intervals are compared with other uncertainty theories, such as lower and upper probabilities, Choquet capacities of order two and belief and plausibility functions. The advantages of probability intervals with respect to these formalisms in computational efficiency are also highlighted.

ATOZET® a New Combination Treatment Option for High-Risk Patients to Lower LDL Cholesterol, is Now Approved in Singapore.

ASEAN+ Rare Disease Network Established.

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The Bare Essentials: Ensuring Affordable Access to Insulin.

AstraZeneca’s CVD-REAL Study Shows SGLT-2 Inhibitors Significantly Reduced Death and Hospitalisations for Heart Failure Versus Other Type-2 Diabetes Medicines.

Most parameter-based online signature verification methods achieve correspondence between the points of two signatures by minimizing the accumulation of their local feature distances. The matching based on minimizing the local feature distances alone is not adequate since the point contains not only local features but the distribution of the remaining points relative to it. One useful way to get correspondences between points on two shapes and measure the similarity of the two shapes is to use the shape context, since this descriptor can be used to describe the distributive relationship between a reference point and the remaining points on a shape. In this paper, we introduce a shape context descriptor for describing an online signature point which contains both 2D spatial information and a time stamp. A common algorithm, dynamic time warp (DTW), is used for the elastic matching between two signatures. When combining shape contexts and local features, we achieve better results than when using only the local features. We evaluate the proposed method on a signature database from the First International Signature Verification Competition (SVC2004). Experimental results demonstrate that the shape context is a good feature and has available complementarity for describing the signature point. The best result by combining the shape contexts and the local features yields an Equal Error Rate (EER) of 6.77% for five references.

The mass of the Higgs boson is measured in the $H\to Z{Z}^{\ast }\to 4\ell$ and in the $H\to \gamma \gamma$ decay channels with $36.1$fb${}^{-1}$ of proton-proton collision data from the Large Hadron Collider at a center-of-mass energy of $\sqrt{s}=13$ TeV recorded by the ATLAS detector in 2015 and 2016. The measured value in the $H\to Z{Z}^{\ast }\to 4\ell$ channel is $m_H^{Z{Z}^{\ast }}=124.88\pm 0.37$GeV, while the measured value in the $H\to \gamma \gamma$ channel is $m_H^{\gamma \gamma }=125.11\pm 0.42$GeV. The two results have a compatibility of $0.4\sigma$. The combined measurement from a simultaneous fit to the invariant mass distributions in the two channels is $m_H=124.98\pm 0.28$GeV.

In this paper, we propose a novel numerical approach for option pricing with the combination of the MC (Monte Carlo) simulation and the PDE (partial differential equation) approach. Our motivation originates from the fact that within a finite life time of an option contract, the underlying price as well as the range of volatility are expected to vary within a relatively small region centered around the current value of the underlying and the volatility and hence there is no need to compute option prices for the underlying and the volatility values beyond this region. Thus, our hybrid approach takes the advantage of both the MC simulation and PDE approach to form an approach that takes the MC simulation as a special case with the region being extremely small and the PDE approach as another special case with the region being extremely large. Through numerical experiments, we demonstrate that such a hybrid approach enhances computational efficiency, while maintaining the same level of accuracy when either the MC simulation or the PDE approach is used alone for the option prices computed within a suitably chosen interested region.

Clustering methods, such as k-means, Fuzzy C-Means (FCM), and others have been developed. However, they are only partitioning a database, so it is difficult to discover the reason why each cluster is formed. This paper proposes a method to discover the knowledge of how the clusters are derived. To select the center vector of each cluster, we employ an unsupervised clustering method based on Self-Organizing Map (SOM) without giving the number of clusters. We define the degree of contribution calculated from the weights of the neural network which learned the center vectors. We then describe the knowledge discovery method from the degrees. We applied our method to the artificial data and the clustering problem. The results show that the degree of contribution is an efficient indicator to represent the knowledge of how the clusters are formed.

Probability theory is the foundation of statistical science, providing a mathematical means of modeling random experiments or uncertainty. Through these mathematical models, researchers are able to draw inferences about the random experiments using observed data. The aim of this chapter is to outline the basic ideas of probability theory that are fundamental to the study of statistics. The entire structure of probability, and therefore of statistics, can be built on the relatively straightforward foundation given in this chapter.