Effects of Flos carthami on CYP2D6 and on the Pharmacokinetics of Metoprolol in Rats

Flos carthami is a traditional Chinese herbal medicine. Clinically, the Flos carthami Injection has been used concomitantly with other Western drugs and may be used concomitantly with β-blockers, such as metoprolol, to treat cerebrovascular and coronary heart diseases, in China. Metoprolol is a CYP2D6 substrate and is predominantly metabolized by this isozyme. However, we do not know whether there is an effect of Flos carthami on CYP2D6 and the consequences of such an effect. Concern is raised regarding the possible herb-drug interaction. In this report, the effects of Flos carthami on the activity of CYP2D6 in vivo and in vitro and on the pharmacokinetics of metoprolol, in rats, are investigated. To assess the inhibitory potency of Flos carthami, the concentration associated with 50% inhibition (IC50) of dextromethorphan metabolism was determined based on the concentration-inhibition curves. The inhibitory effect of Flos carthami on CYP2D6 was also compared with cimetidine in vitro. Flos carthami could significantly inhibit CYP2D6 in rats both in vitro and in vivo (P < .05) and could slow down the metabolic rate of metoprolol as suggested by prolonged t1/2 (67.45%), by increased Cmax (74.51%) and AUC0−∞ (76.89%). These results suggest that CYP2D6 is a risk factor when Flos carthami is administered concomitantly with metoprolol or other CYP2D6 substrates

Regulated proteolysis of the alternative sigma factor SigX in Streptococcus mutans: implication in the escape from competence

BACKGROUND: SigX (σ(X)), the alternative sigma factor of Streptococcus mutans, is the key regulator for transcriptional activation of late competence genes essential for taking up exogenous DNA. Recent studies reveal that adaptor protein MecA and the protease ClpC act as negative regulators of competence by a mechanism that involves MecA-mediated proteolysis of SigX by the ClpC in S. mutans. However, the molecular detail how MecA and ClpC negatively regulate competence in this species remains to be determined. Here, we provide evidence that adaptor protein MecA targets SigX for degradation by the protease complex ClpC/ClpP when S. mutans is grown in a complex medium. RESULTS: By analyzing the cellular levels of SigX, we demonstrate that the synthesis of SigX is transiently induced by competence-stimulating peptide (CSP), but the SigX is rapidly degraded during the escape from competence. A deletion of MecA, ClpC or ClpP results in the cellular accumulation of SigX and a prolonged competence state, while an overexpression of MecA enhances proteolysis of SigX and accelerates the escape from competence. In vitro protein-protein interaction assays confirm that MecA interacts with SigX via its N-terminal domain (NTD(1–82)) and with ClpC via its C-terminal domain (CTD(123–240)). Such an interaction mediates the formation of a ternary SigX-MecA-ClpC complex, triggering the ATP-dependent degradation of SigX in the presence of ClpP. A deletion of the N-terminal or C-terminal domain of MecA abolishes its binding to SigX or ClpC. We have also found that MecA-regulated proteolysis of SigX appears to be ineffective when S. mutans is grown in a chemically defined medium (CDM), suggesting the possibility that an unknown mechanism may be involved in negative regulation of MecA-mediated proteolysis of SigX under this condition. CONCLUSION: Adaptor protein MecA in S. mutans plays a crucial role in recognizing and targeting SigX for degradation by the protease ClpC/ClpP. Thus, MecA actually acts as an anti-sigma factor to regulate the stability of SigX during competence development

Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks

Recently, many profiling side-channel attacks based on Machine Learning and Deep Learning have been proposed. Most of them focus on reducing the number of traces required for successful attacks by optimizing the modeling algorithms. In previous work, relatively sufficient traces need to be used for training a model. However, in the practical profiling phase, it is difficult or impossible to collect sufficient traces due to the constraint of various resources. In this case, the performance of profiling attacks is inefficient even if proper modeling algorithms are used. In this paper, the main problem we consider is how to conduct more efficient profiling attacks when sufficient profiling traces cannot be obtained. To deal with this problem, we first introduce the Conditional Generative Adversarial Network (CGAN) in the context of side-channel attacks. We show that CGAN can generate new traces to enlarge the size of the profiling set, which improves the performance of profiling attacks. For both unprotected and protected cryptographic algorithms, we find that CGAN can effectively learn the leakage of traces collected in their implementations. We also apply it to different modeling algorithms. In our experiments, the model constructed with the augmented profiling set can reduce the required attack traces by more than half, which means the generated traces can provide useful information as the real traces

Coordinated Beamforming with Altruistic Precoding and User Selection for MU-MIMO System

Other cell interference (OCI) degrades the achievable capacity of downlink multiuser multiple-input multiple-output (MU-MIMO) systems seriously. Among OCI mitigation schemes, methods that sacrifice ξ degrees of freedom to nullify the OCI have been proven to be helpful to improve the cell edge throughput. However, since interference nulling schemes can only improve the signal to interference plus noise ratio (SINR) of ξ users, they are not optimal in terms of average cell throughput, especially for low to medium OCI levels. We explore the question whether it is better to improve the SINR of every user in other cells rather than benefit ξ users. An altruistic precoding method to minimize the sum of generated interference for all of the other cell users is proposed with ξ degrees of freedom being sacrificed. With the altruistic precoding method, we deduce the lower bound on the capacity and solve the multicell user selection problem with a local optimal solution in which only eigenvalues of interfering channels are needed to be shared. Simulation results demonstrate that the proposed method outperforms the existing algorithms at any OCI level. Furthermore, we also analyze the best choice of degrees of freedom used to mitigate OCI through simulation

3-D electrical structure and tectonic dynamics in the Yangbajing area based on the array magnetotelluric data

The well-known N-S-trending fault in the Yangbajing area plays a crucial role in the tectonic evolution of the Tibetan Plateau. Previous researches on a few E-W geophysical profiles suggested that the eastern shear at the base of the upper crust and/or lithosphere deformation brought on by asthenosphere upwelling are the major causes of the Yadong-Gulu rift’s creation. Here we propose a 3-D electrical resistivity model derived from the magnetotelluric (MT) array data spanning the Yadong-Gulu rift (YGR), and the distribution of temperature and melt fraction is estimated by the experimental calibrated relationships bridging electrical conductivity and temperature/melt fraction. The result reveals that the Indian slab subducted steeply in the east of the Yadong-Gulu rift, while Indian slab may have delaminated with a flat subduction angle in the west. The temperature distribution shows that the upper mantle of the northern Lhasa terrane is hotter than that of the southern Lhasa terrane. This is likely the result of mantle upwelling caused by either the subduction of the Indian slab or thickened Tibetan lithosphere delamination. Moreover, the strength of the mid-lower crust is so low that it may meet the conditions of the local crust flow in the west-east direction. The local crustal flow and the pulling force from the upwelling asthenosphere jointly contributed to the formation of the Yadong-Gulu rift. These main factors exist in different stages of the evolution of the Yadong-Gulu rift

The deformation mechanism in the western Qiangtang terrane and its surroundings: evidence from magnetotelluric data

Located in the central part of the Tibetan Plateau, the Qiangtang terrane preserves important record of the uplift and deformation history of the Plateau, and therefore remains an attractive area of research. However, deep geophysical investigations of its western part are still limited. To further understand the deep structure of the western Qiangtang terrane and its surroundings, we use magnetotelluric array data to generate a 3D electrical structure. It reveals high resistivity anomalies in the upper crust and scattered high conductivity anomalies in the mid-lower crust. The electrical structure also suggests that the Longmu Co-Gozha Co fault once believed to be a major regional deformation boundary, may not have cut through the crust. The melt content and rheological parameters derived from the electrical structures show dominant ductile-type deformation in most of the study area, which contributes to block extrusion along the slip faults. Viscous deformation regions formed by mantle melt upwelling in the mid-lower crust may contribute to the formation of the N-S directed normal faults on the surface

Energy Efficiency Maximization through Cooperative Transmit and Receive Antenna Selection for Multicell MU-MIMO System

The capacity of Multiple Input Multiple Output (MIMO) system is highly related to the number of active antennas. But as the active antenna number increases, the MIMO system will consume more energy. To maximize the energy efficiency of MIMO system, we propose an antenna selection scheme which can maximize the energy efficiency of BS cluster. In the scheme, ergodic energy efficiency is derived according to large scale channel state information (CSI). Based on this ergodic energy efficiency, we introduce a cost function varied with the number of antennas, in which the effect to the energy efficiency of both the serving BS and the neighbor BS is considered. With this function, we can transform the whole system optimization problem to a sectional optimization problem and obtain a suboptimal antenna set using a heuristic algorithm. Simulation results verify that the proposed approach performs better than the comparison schemes in terms of network energy efficiency and achieves 98% network energy efficiency of the centralized antenna selection scheme. Besides, since the proposed scheme does not need the complete CSI of the neighbor BS, it can effectively reduce the signaling overhead

A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset

Video understanding is an important task in short video business platforms and it has a wide application in video recommendation and classification. Most of the existing video understanding works only focus on the information that appeared within the video content, including the video frames, audio and text. However, introducing common sense knowledge from the external Knowledge Graph (KG) dataset is essential for video understanding when referring to the content which is less relevant to the video. Owing to the lack of video knowledge graph dataset, the work which integrates video understanding and KG is rare. In this paper, we propose a heterogeneous dataset that contains the multi-modal video entity and fruitful common sense relations. This dataset also provides multiple novel video inference tasks like the Video-Relation-Tag (VRT) and Video-Relation-Video (VRV) tasks. Furthermore, based on this dataset, we propose an end-to-end model that jointly optimizes the video understanding objective with knowledge graph embedding, which can not only better inject factual knowledge into video understanding but also generate effective multi-modal entity embedding for KG. Comprehensive experiments indicate that combining video understanding embedding with factual knowledge benefits the content-based video retrieval performance. Moreover, it also helps the model generate better knowledge graph embedding which outperforms traditional KGE-based methods on VRT and VRV tasks with at least 42.36% and 17.73% improvement in HITS@10

A highly sensitive silicon nanowire array sensor for joint detection of tumor markers CEA and AFP

Liver cancer is one of the malignant tumors with the highest fatality rate and increasing incidence, which has no effective treatment plan. Early diagnosis and early treatment of liver cancer play a vital role in prolonging the survival period of patients and improving the cure rate. Carcinoembryonic antigen (CEA) and alpha-fetoprotein (AFP) are two crucial tumor markers for liver cancer diagnosis. In this work, we firstly proposed a wafer-level, highly controlled silicon nanowire (SiNW) field-effect transistor (FET) joint detection sensor for highly sensitive and selective detection of CEA and AFP. The SiNWs-FET joint detection sensor possesses 4 sensing regions. Each sensing region consists of 120 SiNWs arranged in a 15 × 8 array. The SiNW sensor was developed by using a wafer-level and highly controllable top-down manufacturing technology to achieve the repeatability and controllability of device preparation. To identify and detect CEA/AFP, we modified the corresponding CEA antibodies/AFP antibodies to the sensing region surface after a series of surface modification processes, including O2 plasma treatment, soaking in 3-aminopropyltriethoxysilane (APTES) solution, and soaking in glutaraldehyde (GA) solution. The experimental results showed that the SiNW array sensor has superior sensitivity with a real-time ultralow detection limit of 0.1 fg ml−1 (AFP in 0.1× PBS) and 1 fg ml−1 (CEA in 0.1× PBS). Also, the logarithms of the concentration of CEA (from 1 fg ml−1 to 10 pg ml−1) and AFP (from 0.1 fg ml−1 to 100 pg ml−1) achieved conspicuously linear relationships with normalized current changes. The R2 of AFP in 0.1× PBS and R2 of CEA in 0.1× PBS were 0.99885 and 0.99677, respectively. Furthermore, the sensor could distinguish CEA/AFP from interferents at high concentrations. Importantly, even in serum samples, our sensor could successfully detect CEA/AFP. This demonstrates the promising clinical development of our sensor