Flexible electronics devices with tactile perception can sense the mechanical property data of the environment and the human body, and they present a huge potential in the human health system. In particular, the introduction of ultra-flexible and self-powered characteristics to tactile sensors can effectively reduce the problems caused by rigid batteries. Herein, we report a triboelectric nanogenerator (TENG), mainly consisting of an ultra-flexible polydimethylsiloxane (PDMS) film with micro-pyramid-structure and sputtered aluminum electrodes, which achieves highly conformal contact with skin and the self-powered detection of human body motions. The flexible polyethylene terephthalate (PET) film was selected as spacer layer, which made the sensor work in the contact-separation mode and endowed the perfect coupling of triboelectrification and electrostatic induction. Moreover, the controllable and uniform micro-structure PDMS film was fabricated by using the micro-electro-mechanical system (MEMS) manufacturing process, bringing a good sensitivity and high output performance to the device. The developed TENG can directly convert mechanical energy into electric energy and light up 110 green Light-Emitting Diodes (LEDs). Furthermore, the TENG-based sensor displays good sensitivity (2.54 V/kPa), excellent linearity (R2 = 0.99522) and good stability (over 30,000 cycles). By virtue of the compact size, great electrical properties, and great mechanical properties, the developed sensor can be conformally attached to human skin to monitor joint movements, presenting a promising application in wearable tactile devices. We believe that the ultra-flexible and self-powered tactile TENG-based sensor could have tremendous application in wearable electrons.

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BackgroundDiscovering transcription factor binding sites (TFBS) is one of primary challenges to decipher complex gene regulatory networks encrypted in a genome. A set of short DNA sequences identified by a transcription factor (TF) is known as a motif, which can be expressed accurately in matrix form such as a position-specific scoring matrix (PSSM) and a position frequency matrix. Very frequently, we need to query a motif in a database of motifs by seeking its similar motifs, merge similar TFBS motifs possibly identified by the same TF, separate irrelevant motifs, or filter out spurious motifs. Therefore, a novel metric is required to seize slight differences between irrelevant motifs and highlight the similarity between motifs of the same group in all these applications. While there are already several metrics for motif similarity proposed before, their performance is still far from satisfactory for these applications.MethodsA novel metric has been proposed in this paper with name as SPIC (Similarity with Position Information Contents) for measuring the similarity between a column of a motif and a column of another motif. When defining this similarity score, we consider the likelihood that the column of the first motif's PFM can be produced by the column of the second motif's PSSM, and multiply the likelihood by the information content of the column of the second motif's PSSM, and vise versa. We evaluated the performance of SPIC combined with a local or a global alignment method having a function for affine gap penalty, for computing the similarity between two motifs. We also compared SPIC with seven existing state-of-the-arts metrics for their capability of clustering motifs from the same group and retrieving motifs from a database on three datasets.ResultsWhen used jointly with the Smith-Waterman local alignment method with an affine gap penalty function (gap open penalty is equal to1, gap extension penalty is equal to 0.5), SPIC outperforms the seven existing state-of-the-art motif similarity metrics combined with their best alignments for matching motifs in database searches, and clustering the same TF's sub-motifs or distinguishing relevant ones from a miscellaneous group of motifs.ConclusionsWe have developed a novel motif similarity metric that can more accurately match motifs in database searches, and more effectively cluster similar motifs and differentiate irrelevant motifs than do the other seven metrics we are aware of.

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We propose a monospectral image encryption method in which the multispectral color image acquisition by using heterogeneous monospectral cameras. Because the captured monospectral elemental images (EIs) belongs to grayscale image, it is means that the captured EIs can be directly encrypted by the proposed encoding method. Subsequently, the linear cellular automata (CA) and hyperchaotic encoding algorithm are employed to encrypt the captured EIs. Different from previous methods, the proposed method can directly encrypt the multispectral color information rather than having to divide into three color channels (R, G and B), thereby, the proposed method can greatly reduce the encryption calculation. MonospectralEIs encrypted by linear CA and hyper-chaotic system 2.2.1. M-sequence generated by linear CA CA can offer significant benefit over existing algorithms [33,34]. In a one-dimensional (1D), two-state, three-site neighbourhood CA, where the next state of a cell is updated according to its neighborhood, the value of each cell is 0 or 1. The value of each cells can be generated by a specified rule. The value of next state is calculated by the Boolean function with three parameters.where s i (t + 1) denotes the value of cell i at time t + 1, s i (t) denotes the value of the cell i at time t, y i−1 (t) is the the left neighboring cell value at time t, s i+1 (t) is the right neighboring cell value at time t, and F () represents the Boolean function defining a specified rule. According his theory of Wolfram, for the 2-state, 3-site CA, it has 2 8 rules. The Wolfram rules are defined from 0 to 255. Among the rules, eight Wolfram rules are linear. They are 0, 60, 90, 102, 150, 170, 204, and 240, respectively. The eight Wolfram linear rules are represented as follows:Rule 0 : s i (t + 1) = 0, Rule 60 : s i (t + 1) = s i−1 (t) ⊕ s i (t), Rule 90 : s i (t + 1) = s i−1 (t) ⊕ s i+1 (t), Rule 102 : s i (t + 1) = s i (t) ⊕ s i+1 (t), Rule 150 : s i (t + 1) = s i−1 (t) ⊕ s i (t) ⊕ s i+1 (t), Rule 170 : s i (t + 1) = s i−1 (t), Rule 204 : s i (t + 1) = s i (t), Rule 240 : s i (t + 1) = s t−1 (t).

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