A size-dependent inhomogeneous beam model, which accounts for the through-length power-law variation of a two-constituent axially functionally graded (FG) material, is deduced in the framework of the ...nonlocal strain gradient theory and the Euler–Bernoulli beam theory. By employing the Hamilton principle, the equations of motion and boundary conditions for size-dependent axially FG beams are deduced. A material length scale parameter and a nonlocal parameter are introduced in the axially FG beam model to consider the significance of strain gradient stress field and nonlocal elastic stress field, respectively. The bending, buckling and vibration problems of axially FG beams are solved by a generalized differential quadrature method. The influences of power-law variation and size-dependent parameters on the bending, buckling and vibration behaviors of axially FG beams are investigated. The mechanical behaviors can be affected by the through-length grading of the FG material and therefore may be controlled by choosing appropriate values of the power-law index. When considering concentrated and uniformly distributed loads, the maximum deflection decreases with increasing length scale parameter. The axially FG beam may exert a stiffness-softening effect or a stiffness-hardening effect on the critical buckling force and the natural frequencies depending on the values of the two size-dependent parameters.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•Dynamic changes of enzymes in sugar and acid biosynthesis were investigated.•Association between enzymes and major sugars and organic acids was analyzed.•Divergence and different regulation of ...multi-isoforms suggested their specific functions.
In blueberry, sugars and organic acids determine fruit organoleptic quality and drastically change during fruit maturation. This study examined enzymes involved in the metabolism of sugars and organic acids during the three maturation phases (green, pink and blue). During maturation, an increase in sugar (mainly fructose and glucose) was associated with up-regulation of VcSPP (CUFF.32787.1), VcSPS (CUFF.14989.1), and VcINV (gene.g3367.t1.1, CUFF.8077.1 and CUFF.47310.2). A decrease in citrate was associated with VcACLY (CUFF.27347.1 and CUFF.28772.1) in the acetyl-CoA pathway and with VcGAD (CUFF.15663.1 and CUFF.13757.1) and VcGLT (CUFF.6416.1) in the GABA shunt. A decrease in malate was associated with VcMDH (CUFF.30072.1, CUFF.18332.1 and CUFF.24878.1) involved in malate biosynthesis, and with VcADH (gene.g1507.t1.1, CUFF.3210.1 and gene.g30667.t1.1) as well as VcPDC (CUFF.47532.1) involved in fermentation. Multi-isoforms of enzymes were divergent and differentially regulated, suggesting that they have specialized functions in these pathways. The information will contribute to the understanding of blueberry organoleptic quality.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Recent studies have demonstrated that there are significant changes in the gut microbiota (GM) of humans with depression and animal models of depression and chronic stress. In our present study, we ...determined whether an alteration in GM is a decisive factor in anxiety-like and depression-like behavior and its impact on brain neurochemistry. An antibiotic cocktail was used to deplete the GM of mice before they were colonized, via fecal microbiota transplantation (FMT), by the GM of control mice or mice that had been exposed to chronic unpredictable mild stress (CUMS donors). The CUMS-donor group of mice and the mice that were colonized by their microbiota (the CUMS-recipient group) both showed higher levels of anxiety- and depression-like behavior compared to the controls. The GM community of the CUMS-donor and CUMS-recipient was distinctively different from the controls, with the CUMS group characterized by a lower relative abundance of
and a higher relative abundance of
. Interestingly, FMT affected both behavior and neuroinflammation. Mice given the CUMS microbiota had significant elevations of interferon-γ (IFN-γ) and the tumor necrosis factor-alpha (TNF-α) in the hippocampus, which were accompanied by upregulated indoleamine 2,3-dioxygenase 1 (IDO1) in the hippocampus. These results suggest that GM modulates pro-inflammatory cytokines in the hippocampus through dysfunctional microbiota-gut-brain axis, exacerbating anxiety- and depression-like phenotypes. Key Points Chronic unpredictable mild stress increased anxiety- and depression-like behavior in mice. Mice colonized with gut microbiota (GM) from stressed mice showed similar behaviors. The GM composition of the donor and recipient mice was also comparable. Their relative pattern of two bacteria has been tied to neuroinflammatory activity. The results suggest a link between GM, brain function, and anxiety and depression.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Micro-expressions (MEs) are rapid, involuntary facial expressions which reveal emotions that people do not intend to show. Studying MEs is valuable as recognizing them has many important ...applications, particularly in forensic science and psychotherapy. However, analyzing spontaneous MEs is very challenging due to their short duration and low intensity. Automatic ME analysis includes two tasks: ME spotting and ME recognition. For ME spotting, previous studies have focused on posed rather than spontaneous videos. For ME recognition, the performance of previous studies is low. To address these challenges, we make the following contributions: (i) We propose the first method for spotting spontaneous MEs in long videos (by exploiting feature difference contrast). This method is training free and works on arbitrary unseen videos. (ii) We present an advanced ME recognition framework, which outperforms previous work by a large margin on two challenging spontaneous ME databases (SMIC and CASMEII). (iii) We propose the first automatic ME analysis system (MESR), which can spot and recognize MEs from spontaneous video data. Finally, we show our method outperforms humans in the ME recognition task by a large margin, and achieves comparable performance to humans at the very challenging task of spotting and then recognizing spontaneous MEs.
Phosphoric acid-doped polybenzimidazole (PA-PBI) used in high-temperature proton exchange membranes (HT-PEMs) frequently suffers from a serious loss of mechanical strength because of the ...“plasticizing effect” of the dopant acid. Conventional cross-linking approaches generally enhance membrane stability. However, acid doping levels (ADLs) and consequently proton conductivity inevitably decrease. This is due to the formation of more compact molecular structures and a reduced amount of functional imidazole units, caused by their consumption in introducing the cross-linker. To resolve the common problems of current PA-PBI-based HT-PEMs, herein, a highly acidophilic imidazole-rich cross-linked network with superior “antiplasticizing” ability is constructed based on a novel multifunctional cross-linker. This unique bischloro/bibenzimidazole (“A2B2-type”) molecular structure has extremely high reactivity, including “self-reaction” among the cross-linkers and “inter-reaction” between the cross-linker and PBI molecules. The resulting imidazole-rich cross-linked membranes exhibit the desired combination of high ADLs, high conductivity, outstanding dimensional–mechanical stability, and excellent fuel cell performance. In comparison to a corresponding linear PBI membrane, one membrane with a high content of the cross-linker of 30% has a 100 wt % increased acid uptake, a doubling in proton conductivity at 200 °C, and a maximum power density of 533 mW·cm–2 at 160 °C without humidification.
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Heart rate is an important indicator of people's physiological state. Recently, several papers reported methods to measure heart rate remotely from face videos. Those methods work well on stationary ...subjects under well controlled conditions, but their performance significantly degrades if the videos are recorded under more challenging conditions, specifically when subjects' motions and illumination variations are involved. We propose a framework which utilizes face tracking and Normalized Least Mean Square adaptive filtering methods to counter their influences. We test our framework on a large difficult and public database MAHNOB-HCI and demonstrate that our method substantially outperforms all previous methods. We also use our method for long term heart rate monitoring in a game evaluation scenario and achieve promising results.
Face anti-spoofing (FAS) plays a vital role in face recognition systems. Most state-of-the-art FAS methods 1) rely on stacked convolutions and expert-designed network, which is weak in describing ...detailed fine-grained information and easily being ineffective when the environment varies (e.g., different illumination), and 2) prefer to use long sequence as input to extract dynamic features, making them difficult to deploy into scenarios which need quick response. Here we propose a novel frame level FAS method based on Central Difference Convolution (CDC), which is able to capture intrinsic detailed patterns via aggregating both intensity and gradient information. A network built with CDC, called the Central Difference Convolutional Network (CDCN), is able to provide more robust modeling capacity than its counterpart built with vanilla convolution. Furthermore, over a specifically designed CDC search space, Neural Architecture Search (NAS) is utilized to discover a more powerful network structure (CDCN++), which can be assembled with Multiscale Attention Fusion Module (MAFM) for further boosting performance. Comprehensive experiments are performed on six benchmark datasets to show that 1) the proposed method not only achieves superior performance on intra-dataset testing (especially 0.2% ACER in Protocol-1 of OULU-NPU dataset), 2) it also generalizes well on cross-dataset testing (particularly 6.5% HTER from CASIA-MFSD to Replay-Attack datasets). The codes are available at https://github.com/ZitongYu/CDCN.
A robust automatic micro-expression recognition system would have broad applications in national safety, police interrogation, and clinical diagnosis. Developing such a system requires high quality ...databases with sufficient training samples which are currently not available. We reviewed the previously developed micro-expression databases and built an improved one (CASME II), with higher temporal resolution (200 fps) and spatial resolution (about 280×340 pixels on facial area). We elicited participants' facial expressions in a well-controlled laboratory environment and proper illumination (such as removing light flickering). Among nearly 3000 facial movements, 247 micro-expressions were selected for the database with action units (AUs) and emotions labeled. For baseline evaluation, LBP-TOP and SVM were employed respectively for feature extraction and classifier with the leave-one-subject-out cross-validation method. The best performance is 63.41% for 5-class classification.
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