Temporal Interaction -- Bridging Time and Experience in Human-Computer Interaction

Traditional static user interfaces (UI) have given way to dynamic systems that can intelligently adapt to and respond to users' changing needs. Temporal interaction is an emerging field in human-computer interaction (HCI), which refers to the study and design of UI that are capable of adapting and responding to the user's changing behavioral and emotional states. By comprehending and incorporating the temporal component of user interactions, it focuses on developing dynamic and individualized user experiences. This idea places a strong emphasis on the value of adjusting to user behavior and emotions in order to create a more unique and interesting user experience. The potential of temporal interaction to alter user interface design is highlighted by this paper's examination of its capacity to adjust to user behavior and react to emotional states. Designers can create interfaces that respond to the changing demands, emotions, and behaviors of users by utilizing temporal interactions. This produces interfaces that are not only highly functional but also form an emotional connection with the users.Comment: 8 page

De novo synthesis of trans-10, cis-12 conjugated linoleic acid in oleaginous yeast Yarrowia Lipolytica

<p>Abstract</p> <p>Background</p> <p>Conjugated linoleic acid (CLA) has many well-documented beneficial physiological effects. Due to the insufficient natural supply of CLA and low specificity of chemically produced CLA, an effective and isomer-specific production process is required for medicinal and nutritional purposes.</p> <p>Results</p> <p>The linoleic acid isomerase gene from <it>Propionibacterium acnes</it> was expressed in <it>Yarrowia lipolytica</it> Polh. Codon usage optimization of the PAI and multi-copy integration significantly improved the expression level of PAI in <it>Y. lipolytica.</it> The percentage of <it>trans</it>-10, <it>cis</it>-12 CLA was six times higher in yeast carrying the codon-optimized gene than in yeast carrying the native gene. In combination with multi-copy integration, the production yield was raised to approximately 30-fold. The amount of <it>trans</it>-10, <it>cis</it>-12 CLA reached 5.9% of total fatty acid yield in transformed <it>Y. lipolytica</it>.</p> <p>Conclusions</p> <p>This is the first report of production of <it>trans</it>-10, <it>cis</it>-12 CLA by the oleaginous yeast <it>Y. lipolytica</it>, using glucose as the sole carbon source through expression of linoleic acid isomerase from <it>Propionibacterium acnes</it>.</p

The impact of transport inclusion on active Aging: A perceived value analysis

As global aging accelerates, serving an aging society through transport development is essential. However, the impact of transport inclusion on active aging remains unclear, especially regarding travel satisfaction and capabilities. The present work establishes a relationship model between transport inclusion perceived value (TIPV) and active aging (AA) that accounts for travel satisfaction (S) and travel capabilities (C). A theoretical framework is proposed for TIPV and AA by exploring the mediation mechanism and regulatory factors that affect AA. The data of this study are derived from the survey data of 521 people over 60 years old in Xi'an in 2022. The study identifies four TIPV dimensions: perceived functional, service, emotional, and social values, the latter two of which are often overlooked; it reveals the mediating role of travel satisfaction in the TIPV-AA relationship with an effect on health, social participation, and subjective well-being. This first sensitivity analysis of transport-inclusive travel capabilities offers a theoretical foundation for understanding the four transport inclusion dimensions and practical guidance for creating inclusive transport environments

SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogates

CNN-based surrogates have become prevalent in scientific applications to replace conventional time-consuming physical approaches. Although these surrogates can yield satisfactory results with significantly lower computation costs over small training datasets, our benchmarking results show that data-loading overhead becomes the major performance bottleneck when training surrogates with large datasets. In practice, surrogates are usually trained with high-resolution scientific data, which can easily reach the terabyte scale. Several state-of-the-art data loaders are proposed to improve the loading throughput in general CNN training; however, they are sub-optimal when applied to the surrogate training. In this work, we propose SOLAR, a surrogate data loader, that can ultimately increase loading throughput during the training. It leverages our three key observations during the benchmarking and contains three novel designs. Specifically, SOLAR first generates a pre-determined shuffled index list and accordingly optimizes the global access order and the buffer eviction scheme to maximize the data reuse and the buffer hit rate. It then proposes a tradeoff between lightweight computational imbalance and heavyweight loading workload imbalance to speed up the overall training. It finally optimizes its data access pattern with HDF5 to achieve a better parallel I/O throughput. Our evaluation with three scientific surrogates and 32 GPUs illustrates that SOLAR can achieve up to 24.4X speedup over PyTorch Data Loader and 3.52X speedup over state-of-the-art data loaders.Comment: 14 pages, 15 figures, 5 tables, submitted to VLDB '2

Genome Characterization of the Oleaginous Fungus Mortierella alpina

Mortierella alpina is an oleaginous fungus which can produce lipids accounting for up to 50% of its dry weight in the form of triacylglycerols. It is used commercially for the production of arachidonic acid. Using a combination of high throughput sequencing and lipid profiling, we have assembled the M. alpina genome, mapped its lipogenesis pathway and determined its major lipid species. The 38.38 Mb M. alpina genome shows a high degree of gene duplications. Approximately 50% of its 12,796 gene models, and 60% of genes in the predicted lipogenesis pathway, belong to multigene families. Notably, M. alpina has 18 lipase genes, of which 11 contain the class 2 lipase domain and may share a similar function. M. alpina's fatty acid synthase is a single polypeptide containing all of the catalytic domains required for fatty acid synthesis from acetyl-CoA and malonyl-CoA, whereas in many fungi this enzyme is comprised of two polypeptides. Major lipids were profiled to confirm the products predicted in the lipogenesis pathway. M. alpina produces a complex mixture of glycerolipids, glycerophospholipids and sphingolipids. In contrast, only two major sterol lipids, desmosterol and 24(28)-methylene-cholesterol, were detected. Phylogenetic analysis based on genes involved in lipid metabolism suggests that oleaginous fungi may have acquired their lipogenic capacity during evolution after the divergence of Ascomycota, Basidiomycota, Chytridiomycota and Mucoromycota. Our study provides the first draft genome and comprehensive lipid profile for M. alpina, and lays the foundation for possible genetic engineering of M. alpina to produce higher levels and diverse contents of dietary lipids

Gaussian Process Regression-Based Data-Driven Material Models for Stochastic Structural Analysis

The data-driven material models have attracted many researchers recently, as they could directly use material data. However, there are limited studies about material uncertainty in previous data-driven models. This thesis proposes a new Gaussian Process Regression (GPR)-based approach to capture the material behaviour and the associated material uncertainty from the dataset. The GPR approach is firstly used for the nonlinear elastic behaviour. The obtained GPR-based model is verified by the material datasets. Then, an improved GPR model, called the Heteroscedastic Sparse Gaussian Process Regression (HSGPR) model, is applied for the plastic flow behaviour. The flow stress predicted by the HSGPR model also agrees with the experiments. As a new data-driven material model is introduced, the associated frameworks, which implement the GPR-based model and HSGPR-based model into the finite element method for structural reliability analysis, are developed. The frame problem is used to demonstrate the GPR-based model in the elastic stochastic structural analysis, while the beam and punch problems validate the HSGPR-based model in the plastic stochastic structural analysis. It is concluded that the GPR-based approach can accurately identify both the elastic and plastic stochastic structural responses. To consider the possible correlation of the stochastic material behaviours, a novel GPR-based approach, which combines the HSGPR model with the Proper Orthogonal Decomposition (POD) algorithm, is proposed. Two case studies on the metal strength and the rock joint behaviour have demonstrated that the material behaviours correlation can be effectively retained in the POD-HSGPR-based model. As indicated by its application in a rock slope problem, it is critical to consider the material properties correlation for the accurate evaluation of structural reliability

Lonicera Caerulea Juice Alleviates Alcoholic Liver Disease by Regulating Intestinal Flora and the FXR-FGF15 Signaling Pathway

Alcoholic liver disease (ALD) is a growing public health issue with high financial, social, and medical costs. Lonicera caerulea, which is rich in polyphenolic compounds, has been shown to exert anti-oxidative and anti–inflammatory effects. This study aimed to explore the effects and mechanisms of concentrated Lonicera caerulea juice (LCJ) on ALD in mice. ALD was established in mice via gradient alcohol feeding for 30 days. The mice in the experimental group were given LCJ by gavage. The reduction of aspartate transaminase (AST) and alanine transaminase (ALT) in the serum of mice indicated that LCJ has a liver-protective effect. LCJ improved the expression of AMPK, PPARα, and CPT1b in ALD mice to reduce the liver lipid content. Additionally, LCJ increased the expression of farnesoid X receptor (FXR), fibroblast growth factor 15 (FGF15), and fibroblast growth factor receptor 4 (FGFR4), which lowers the expression of cytochrome P450 7A1 (CYP7A1) and lessens bile acid deposition in the liver. In mice, LCJ improved the intestinal barrier by upregulating the expression of mucins and tight junction proteins in the small intestine. Moreover, it accelerated the restoration of microbial homeostasis in both the large and small intestines and increased short–chain fatty acids in the cecum. In conclusion, LCJ alleviates ALD by reducing liver and serum lipid accumulation and modulating the FXR–FGF15 signaling pathway mediated by gut microbes

Tui si xuan shi ji.

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Enhanced Soluble Expression of Linoleic Acid Isomerase by Coordinated Regulation of Promoter and Fusion Tag in <i>Escherichia coli</i>

PAI is a linoleic acid isomerase from Propionibacterium acnes and is the key enzyme in the synthesis of trans10, cis12-conjugated linoleic acid. However, the majority of the expressed PAI in Escherichia coli occurs in its nonfunctional form in inclusion bodies, limiting the biosynthesis of conjugated linoleic acid. In an attempt to improve the solubility of recombinant PAI in Escherichia coli, three promoters representing different transcriptional strengths (T7, CspA, and Trc), paired with three fusion tags, (His6, MBP, and Fh8), respectively, were investigated in this study. Among the nine recombinant strains, Escherichia coli BL21 (DE3) (pET24a-Mpai), containing the T7 promoter and MBP fusion tag, led to a considerable increase in PAI solubility to 86.2%. MBP-PAI was purified 41-fold using affinity column chromatography. The optimum catalytical conditions of MBP-PAI were 37 °C and pH 7.5 with the addition of 1 mmol/L Tween-20. Most of the tested metal ions inhibited MBP-PAI activity. The apparent kinetic parameters (Km and Vmax) were measured with linoleic acid concentrations ranging from 71 μM to 1428 μM. The substrate linoleic acid did not exert any inhibitory effect on MBP-PAI. The Km of MBP-PAI was 253.9 μmol/L, and the Vmax was 2253 nmol/min/mg. This study provided a new method for improving the solubility of the recombinant linoleic acid isomerase in Escherichia coli