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Conclusions The thresholds elicited in response to the fluid in the meatus likely reflect a form of STC and may result from excitation of the inner ear by the vibrations induced in the fluid. The sloping fluid audiograms may reflect transmission pathways that are less effective at higher frequencies.Polyploid plants are more often invasive species than their diploid counterparts. As the invasiveness of a species is often linked to its production of allelopathic compounds, we hypothesize that differences in invasive ability between cytotypes may be due to their different ability to synthesize allelopathic metabolites. We test this using two cytotypes of Solidago canadensis as the model and use integrated metabolome and transcriptome data to resolve the question. Metabolome analysis identified 122 metabolites about flavonoids, phenylpropanoids and terpenoids, of which 57 were differentially accumulated between the two cytotypes. Transcriptome analysis showed that many differentially expressed genes (DEGs) were enriched in 'biosynthesis of secondary metabolites', 'plant hormone signal transduction', and 'MAPK signaling', covering most steps of plant allelopathic metabolite synthesis. Importantly, the differentially accumulated flavonoids, phenylpropanoids and terpenoids were closely correlated with related DEGs. Furthermore, 30 miRNAs were found to be negatively associated with putative targets, and they were thought to be involved in target gene expression regulation. These miRNAs probably play a vital role in the regulation of metabolite synthesis in hexaploid S. canadensis. The two cytotypes of S. canadensis differ in the allelopathic metabolite synthesis and this difference is associated with regulation of expression of a range of genes. These results suggest that changes in gene expression may underlying the increased invasive potential of the polyploidy.Breast cancer resistance protein (BCRP), an ATP-binding cassette (ABC) half transporter encoded by the Abcg2 gene, is reported to influence the pharmacokinetics of substrate drugs during clinical therapy. The aim of this study was to clarify the mechanisms that regulate the transcription of the chicken Abcg2 gene through cloning and characterization of its promoter region. Results showed that the Abcg2 gene is transcribed by a TATA-less promoter with several putative Sp1 sites upstream from two putative CpG islands. A luciferase reporter assay conducted both in chicken leghorn male hepatoma (LMH) cells and chicken primary hepatocytes mapped a basal promoter to nucleotides -110 to +30, which is responsible for the constitutive expression of Abcg2. The 5'-region upstream of the basal promoter was characterized by both positive and negative regulatory domains. Further, using the cell-based reporter gene assay combined with RT-PCR and drug accumulation analysis, we found that four xenobiotics, daidzein, clotrimazole, ivermectin, and lipopolysaccharide (LPS), influence the expression and function of BCRP through significant regulation of the Abcg2 gene promoter. Interaction sites with the Abcg2 gene promoter of these four selected regulators were clarified by progressive deletions and mutation assays. This study shed some light on the regulatory mechanisms involved in chicken Abcg2 gene expression and the results may have far-reaching significance regarding the usage and development of veterinary drugs.Sensor differential signals are widely used in many systems. The tracking differentiator (TD) is an effective method to obtain signal differentials. Differential calculation is noise-sensitive. There is the characteristics of low-pass filter (LPF) in the TD to suppress the noise, but phase lag is introduced. For LPF, fixed filtering parameters cannot achieve both noise suppression and phase compensation lag compensation. We propose a fuzzy self-tuning tracking differentiator (FSTD) capable of adaptively adjusting parameters, which uses the frequency information of the signal to achieve a trade-off between the phase lag and noise suppression capabilities. Based on the frequency information, the parameters of TD are self-tuning by a fuzzy method, which makes self-tuning designs more flexible. Simulations and experiments using motion measurement sensors show that the proposed method has good filtering performance for low-frequency signals and improves tracking ability for high-frequency signals compared to fixed-parameter differentiator.Traditional calibration method is usually performed with expensive equipments suchas three-axis turntable in a laboratory environment. However in practice, in order to ensure theaccuracy and stability of the inertial navigation system (INS), it is usually necessary to recalibratethe inertial measurement unit (IMU) without external equipment in the field. In this paper, anew in-field recalibration method for triaxial accelerometer based on beetle swarm antenna search(BSAS) algorithm is proposed. Firstly, as a new intelligent optimization algorithm, BSAS algorithmand its improvements based on basic beetle antennae search (BAS) algorithm are introduced indetail. Secondly, the nonlinear mathematical model of triaxial accelerometer is established forhigher calibration accuracy, and then 24 optimal measurement positions are designed by theoreticalanalysis. In addition, the calibration procedures are improved according to the characteristics of BSASalgorithm, then 15 calibration parameters in the nonlinear method are optimized by BSAS algorithm.Besides, the results of BSAS algorithm and basic BAS algorithm are compared by simulation, whichshows the priority of BSAS algorithm in calibration field. Finally, two experiments demonstrate thatthe proposed method can achieve high precision in-field calibration without any external equipment,and meet the accuracy requirements of the INS.Person re-identification (re-ID) is among the essential components that play an integral role in constituting an automated surveillance environment. Majorly, the problem is tackled using data acquired from vision sensors using appearance-based features, which are strongly dependent on visual cues such as color, texture, etc., consequently limiting the precise re-identification of an individual. To overcome such strong dependence on visual features, many researchers have tackled the re-identification problem using human gait, which is believed to be unique and provide a distinctive biometric signature that is particularly suitable for re-ID in uncontrolled environments. However, image-based gait analysis often fails to extract quality measurements of an individual's motion patterns owing to problems related to variations in viewpoint, illumination (daylight), clothing, worn accessories, etc. To this end, in contrast to relying on image-based motion measurement, this paper demonstrates the potential to re-identify an individual using inertial measurements units (IMU) based on two common sensors, namely gyroscope and accelerometer. The experiment was carried out over data acquired using smartphones and wearable IMUs from a total of 86 randomly selected individuals including 49 males and 37 females between the ages of 17 and 72 years. The data signals were first segmented into single steps and strides, which were separately fed to train a sequential deep recurrent neural network to capture implicit arbitrary long-term temporal dependencies. The experimental setup was devised in a fashion to train the network on all the subjects using data related to half of the step and stride sequences only while the inference was performed on the remaining half for the purpose of re-identification. The obtained experimental results demonstrate the potential to reliably and accurately re-identify an individual based on one's inertial sensor data.Abnormal falls in public places have significant safety hazards and can easily lead to serious consequences, such as trampling by people. Vision-driven fall event detection has the huge advantage of being non-invasive. However, in actual scenes, the fall behavior is rich in diversity, resulting in strong instability in detection. Based on the study of the stability of human body dynamics, the article proposes a new model of human posture representation of fall behavior, called the "five-point inverted pendulum model", and uses an improved two-branch multi-stage convolutional neural network (M-CNN) to extract and construct the inverted pendulum structure of human posture in real-world complex scenes. Furthermore, we consider the continuity of the fall event in time series, use multimedia analytics to observe the time series changes of human inverted pendulum structure, and construct a spatio-temporal evolution map of human posture movement. Finally, based on the integrated results of computer vision and multimedia analytics, we reveal the visual characteristics of the spatio-temporal evolution of human posture under the potentially unstable state, and explore two key features of human fall behavior motion rotational energy and generalized force of motion. The experimental results in actual scenes show that the method has strong robustness, wide universality, and high detection accuracy.Privacy enhancing technologies (PETs) allow to achieve user's transactions unlinkability across different online Service Providers. However, current PETs fail to guarantee unlinkability against the Identity Provider (IdP), which becomes a single point of failure in terms of privacy and security, and therefore, might impersonate its users. To address this issue, OLYMPUS EU project establishes an interoperable framework of technologies for a distributed privacy-preserving identity management based on cryptographic techniques that can be applied both to online and offline scenarios. Namely, distributed cryptographic techniques based on threshold cryptography are used to split up the role of the Identity Provider (IdP) into several authorities so that a single entity is not able to impersonate or track its users. The architecture leverages PET technologies, such as distributed threshold-based signatures and privacy attribute-based credentials (p-ABC), so that the signed tokens and the ABC credentials are managed in a distributed way by several IdPs. This paper describes the Olympus architecture, including its associated requirements, the main building blocks and processes, as well as the associated use cases. selleck chemical In addition, the paper shows how the Olympus oblivious architecture can be used to achieve privacy-preserving M2M offline transactions between IoT devices.There has been growing interest in using strong field enhancement and light localization in plasmonic nanostructures to control the polarization properties of light. Various experimental techniques are now used to fabricate twisted metallic nanoparticles and metasurfaces, where strongly enhanced chiral near-fields are used to intensify circular dichroism (CD) signals. In this review, state-of-the-art strategies to develop such chiral plasmonic nanoparticles and metasurfaces are summarized, with emphasis on the most recent trends for the design and development of functionalizable surfaces. The major objective is to perform enantiomer selection which is relevant in pharmaceutical applications and for biosensing. Enhanced sensing capabilities are key for the design and manufacture of lab-on-a-chip devices, commonly named point-of-care biosensing devices, which are promising for next-generation healthcare systems.