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From the obtained experimental results, it is evident that our proposed neural network was capable of separating trucks from other vehicles, with an accuracy of 94.9%, and classifying vehicles into three different classes, with an accuracy of 70.8%. Based on the experimental results, extending sensor arrays as described in the last part of the paper is recommended.Deep sequencing technologies have revealed the once uncharted non-coding transcriptome of circular RNAs (circRNAs). Despite the lack of protein-coding potential, these unorthodox yet highly stable RNA species are known to act as critical gene regulatory hubs, particularly in malignancies. However, their mechanistic implications in tumor outcome and translational potential have not been fully resolved. Using RNA-seq data, we profiled the circRNAomes of tumor specimens derived from oral squamous cell carcinoma (OSCC), which is a prevalently diagnosed cancer with a persistently low survival rate. We further catalogued dysregulated circRNAs in connection with tumorigenic progression. Using comprehensive bioinformatics analyses focused on co-expression maps and miRNA-interaction networks, we delineated the regulatory networks that are centered on circRNAs. Interestingly, we identified a tumor-associated, pro-tumorigenic circRNA, named circFLNB, that was implicated in maintaining several tumor-associated phenotypes in vitro and in vivo. Correspondingly, transcriptome profiling of circFLNB-knockdown cells showed alterations in tumor-related genes. selleck inhibitor Integrated in silico analyses further deciphered the circFLNB-targeted gene network. Together, our current study demarcates the OSCC-associated circRNAome, and unveils a novel circRNA circuit with functional implication in OSCC progression. These systems-based findings broaden mechanistic understanding of oral malignancies and raise new prospects for translational medicine.Due to the possible applications, materials with a wide energy gap are becoming objects of interest for researchers and engineers. In this context, the polycrystalline diamond layers grown by CVD methods on silicon substrates seem to be a promising material for engineering sensing devices. The proper tuning of the deposition parameters allows us to develop the diamond layers with varying crystallinity and defect structure, as was shown by SEM and Raman spectroscopy investigations. The cathodoluminescence (CL) spectroscopy revealed defects located just in the middle of the energy gap of diamonds. The current-voltage-temperature, I-V-T characteristics performed in a broad temperature range of 77-500 K yielded useful information about the electrical conduction in this interesting material. The recorded I-V-T in the forward configuration of the n-Si/p-CVD diamond heterojunction indicated hopping trough defects as the primary mechanism limiting conduction properties. The Ohmic character of the carriers flux permitting throughout heterojunction is intensified by charges released from the depletion layer. The magnification amplitude depends on both the defect density and the probability that biasing voltage is higher than the potential barrier binding the charge. In the present work, a simple model is proposed that describes I-V-T characteristics in a wide range of voltage, even where the current saturation effect occurs.In this work, we propose an online method to detect and approximately locate an external load induced on the body of a person interacting with the environment. The method is based on a torque equilibrium condition on the human sagittal plane, which takes into account a reduced-complexity model of the whole-body centre of pressure (CoP) along with the measured one, and the vertical component of the ground reaction forces (vGRFs). The latter is combined with a statistical analysis approach to improve the localisation accuracy, (which is subject to uncertainties) to the extent of the industrial applications we target. The proposed technique eliminates the assumption of known contact position of an external load on the human limbs, allowing a more flexible online body-state tracking. The accuracy of the proposed method is first evaluated via a simulation study in which various contact points on different body postures are considered. Next, experiments on human subjects with three different contact locations applied to the human body are presented, revealing the validity of the proposed methodology. Lastly, its benefit in the estimation of human dynamic states is demonstrated. These results add another layer to the online human ergonomics assessment framework developed in our laboratory, extending it to more realistic and varying interaction conditions.In this paper, a novel dynamic Vision-Based Measurement method is proposed to measure contact force independent of the object sizes. A neuromorphic camera (Dynamic Vision Sensor) is utilizused to observe intensity changes within the silicone membrane where the object is in contact. Three deep Long Short-Term Memory neural networks combined with convolutional layers are developed and implemented to estimate the contact force from intensity changes over time. Thirty-five experiments are conducted using three objects with different sizes to validate the proposed approach. We demonstrate that the networks with memory gates are robust against variable contact sizes as the networks learn object sizes in the early stage of a grasp. Moreover, spatial and temporal features enable the sensor to estimate the contact force every 10 ms accurately. The results are promising with Mean Squared Error of less than 0.1 N for grasping and holding contact force using leave-one-out cross-validation method.

The species

includes cultivated varieties (subsp.

var.

), wild plants (subsp.

var.

), and five other subspecies spread over almost all continents. Single nucleotide polymorphisms in the expressed sequence tag able to underline intra-species differentiation are not yet identified, beyond a few plastidial markers.

In the present work, more than 1000 transcript-specific SNP markers obtained by the genotyping of 260 individuals were studied. These genotypes included cultivated, oleasters, and samples of subspecies

, and were analyzed in silico, in order to identify polymorphisms on key genes distinguishing different

forms.

Phylogeny inference and principal coordinate analysis allowed to detect two distinct clusters, clearly separating wilds and

samples from cultivated olives, meanwhile the structure analysis made possible to differentiate these three groups. Sequences carrying the polymorphisms that distinguished wild and cultivated olives were analyzed and annotated, allowing to identify 124 candidate genes that have a functional role in flower development, stress response, or involvement in important metabolic pathways.