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Facial expression conveys nonverbal communication information to help humans better perceive physical or psychophysical situations. Accurate 3D imaging provides stable topographic changes for reading facial expression. In particular, light‐field cameras (LFCs) have high potential for constructing depth maps, thanks to a simple configuration of microlens arrays and an objective lens. Herein, machine‐learned NIR‐based LFCs (NIR‐LFCs) for facial expression reading by extracting Euclidean distances of 3D facial landmarks in pairwise fashion are reported. The NIR‐LFC contains microlens arrays with asymmetric Fabry−Perot filter and NIR bandpass filter on CMOS image sensor, fully packaged with two vertical‐cavity surface‐emitting lasers. The NIR‐LFC not only increases the image contrast by 2.1 times compared with conventional LFCs, but also reduces the reconstruction errors by up to 54%, regardless of ambient illumination conditions. A multilayer perceptron (MLP) classifies input vectors, consisting of 78 pairwise distances on the facial depth map of happiness, anger, sadness, and disgust, and also exhibits exceptional average accuracy of 0.85 (p<0.05). This LFC provides a new platform for quantitatively labeling facial expression and emotion in point‐of‐care biomedical, social perception, or human−machine interaction applications. [ABSTRACT FROM AUTHOR]

Abstract Background Cells coordinate their metabolism, proliferation, and cellular communication according to environmental cues through signal transduction. Because signal transduction has a primary role in cellular processes, many experimental techniques and approaches have emerged to discover the molecular components and dynamics that are dependent on cellular contexts. However, omics approaches based on genome-wide expression analysis data comparing one differing condition (e.g. complex disease patients and normal subjects) did not investigate the dynamics and inter-pathway cross-communication that are dependent on cellular contexts. Therefore, we introduce a new computational omics approach for discovering signal transduction pathways regulated by transcription and transcriptional regulations between pathways in signaling networks that are dependent on cellular contexts, especially focusing on a transcription-mediated mechanism of inter-pathway cross-communication. Results Applied to dendritic cells treated with lipopolysaccharide, our analysis well depicted how dendritic cells respond to the treatment through transcriptional regulations between signal transduction pathways in dendritic cell maturation and T cell activation. Conclusions Our new approach helps to understand the underlying biological phenomenon of expression data (e.g. complex diseases such as cancer) by providing a graphical network which shows transcriptional regulations between signal transduction pathways. The software programs are available upon request.

Abstract Background A significant portion (about 8% in the human genome) of mammalian mRNA sequences contains AU (Adenine and Uracil) rich elements or AREs at their 3' untranslated regions (UTR). These mRNA sequences are usually stable. However, an increasing number of observations have been made of unstable species, possibly depending on certain elements such as Alu repeats. ARE motifs are repeats of the tetramer AUUU and a monomer A at the end of the repeats ((AUUU)nA). The importance of AREs in biology is that they make certain mRNA unstable. Proto-oncogene, such as c-fos, c-myc, and c-jun in humans, are associated with AREs. Although it has been known that the increased number of ARE motifs caused the decrease of the half-life of mRNA containing ARE repeats, the exact mechanism is as of yet unknown. We analyzed the occurrences of AREs and Alu and propose a possible mechanism for how human mRNA could acquire and keep AREs at its 3' UTR originating from Alu repeats. Results Interspersed in the human genome, Alu repeats occupy 5% of the 3' UTR of mRNA sequences. Alu has poly-adenine (poly-A) regions at its end, which lead to poly-thymine (poly-T) regions at the end of its complementary Alu. It has been found that AREs are present at the poly-T regions. From the 3' UTR of the NCBI's reference mRNA sequence database, we found nearly 40% (38.5%) of ARE (Class I) were associated with Alu sequences (Table 1) within one mismatch allowance in ARE sequences. Other ARE classes had statistically significant associations as well. This is far from a random occurrence given their limited quantity. At each ARE class, random distribution was simulated 1,000 times, and it was shown that there is a special relationship between ARE patterns and the Alu repeats. Table 1 Defined ARE classes. (Symbol marks are used in this study instead of full sequences.) Symbol ARE sequence Class I (AUUU)5A AUUUAUUUAUUUAUUUAUUUA Class II (AUUU)4A AUUUAUUUAUUUAUUUA Class III U(AUUU)3AU UAUUUAUUUAUUUAU Class IV UU(AUUU)2AUU UUAUUUAUUUAUU Class V U4AUUUAU4 UUUUAUUUAUUUU Class VI W3UAUUUAUW3 WWWUAUUUAWWW Conclusion AREs are mediating sequence elements affecting the stabilization or degradation of mRNA at the 3' untranslated regions. However, AREs' mechanism and origins are unknown. We report that Alu is a source of ARE. We found that half of the longest AREs were derived from the poly-T regions of the complementary Alu.