Oxidative stress and age-related cataract

Age-related cataract is a clouding of the lens that leads to decreased vision. It increases with age and is one of the leading causes of blindness worldwide. The only treatment currently available is surgery. Therefore, it is important to identify modifiable risk factors for cataract prevention. The cause of cataract is not fully understood and may be multifactorial, involving oxidative stress, a condition of disrupted balance between oxidants and antioxidants. Oxidative damage to lens proteins and lipids is suggested to be involved in the development of cataract. Antioxidants may protect against oxidative damage. The aim of this thesis was to examine factors related to oxidative stress, including biomarkers of exogenous/dietary and endogenous antioxidants, and systemic oxidative stress and inflammation, as well as vitamin supplement use and physical activity, with the risk of age-related cataract. The studies were based on women and men, born 1914-1952, in the population-based Swedish Mammography Cohort and the Cohort of Swedish Men. Information on diet and lifestyle factors was obtained from a self-administered questionnaire at baseline. Cases of age-related cataract were identified through linkage to registers. The relationship between exogenous/dietary and endogenous antioxidants was examined in a cross-sectional study of women with and without a history of chronic diseases. High fruit and vegetable intake and high levels of plasma carotenoids were associated with lower plasma extracellular superoxide dismutase activity (an endogenous antioxidant enzyme) in healthy women but not in women with a history of chronic diseases. In a nested case-control study including women with and without incident cataract, higher levels of urinary 8-iso-prostaglandin F2α (a biomarker for systemic oxidative stress) were associated with increased risk of cataract, but no association was observed for 15-keto-dihydro-prostaglandin F2α (a biomarker for systemic inflammation). The association between dietary supplement use and risk of cataract was investigated prospectively in the cohorts. The use of single, high-dose supplements of vitamin C or E, as well as B vitamins, but not multivitamins (usually containing vitamin doses close to recommended daily intake), was associated with increased risk of cataract. The use of vitamin C supplements in combination with some oxidative stress-related factors, such as age and corticosteroid use, as well as in the long-term, may be associated with even higher risk. The association between physical activity and risk of cataract was also examined prospectively. Higher levels of total physical activity, especially long-term, and specific subtypes including walking/bicycling and work/occupational activity, were associated with lower risk of cataract in women and men. Conversely, high leisure time inactivity levels were associated with increased risk of cataract. In conclusion, these results suggest that maintaining low systemic oxidative stress by having a healthier lifestyle, including eating a diet rich in antioxidants instead of taking high-dose supplements and being physically active may prevent cataract development in the general population

Recursive Generalization Transformer for Image Super-Resolution

Transformer architectures have exhibited remarkable performance in image super-resolution (SR). Since the quadratic computational complexity of the self-attention (SA) in Transformer, existing methods tend to adopt SA in a local region to reduce overheads. However, the local design restricts the global context exploitation, which is crucial for accurate image reconstruction. In this work, we propose the Recursive Generalization Transformer (RGT) for image SR, which can capture global spatial information and is suitable for high-resolution images. Specifically, we propose the recursive-generalization self-attention (RG-SA). It recursively aggregates input features into representative feature maps, and then utilizes cross-attention to extract global information. Meanwhile, the channel dimensions of attention matrices (query, key, and value) are further scaled to mitigate the redundancy in the channel domain. Furthermore, we combine the RG-SA with local self-attention to enhance the exploitation of the global context, and propose the hybrid adaptive integration (HAI) for module integration. The HAI allows the direct and effective fusion between features at different levels (local or global). Extensive experiments demonstrate that our RGT outperforms recent state-of-the-art methods quantitatively and qualitatively. Code is released at https://github.com/zhengchen1999/RGT.Comment: Code is available at https://github.com/zhengchen1999/RG

Stabilization computation for a kind of uncertain switched systems using non-fragile sliding mode observer method

A non-fragile sliding mode control problem will be investigated in this article. The problem focuses on a kind of uncertain switched singular time-delay systems in which the state is not available. First, according to the designed non-fragile observer, we will construct an integral-type sliding surface, in which the estimated unmeasured state is used. Second, we synthesize a sliding mode controller. The reachability of the specified sliding surface could be proved by this sliding mode controller in a finite time. Moreover, linear matrix inequality conditions will be developed to check the exponential admissibility of the sliding mode dynamics. After that, the gain matrices designed will be given along with it. Finally, some numerical result will be provided, and the result can be used to prove the effectiveness of the method

Cross Aggregation Transformer for Image Restoration

Recently, Transformer architecture has been introduced into image restoration to replace convolution neural network (CNN) with surprising results. Considering the high computational complexity of Transformer with global attention, some methods use the local square window to limit the scope of self-attention. However, these methods lack direct interaction among different windows, which limits the establishment of long-range dependencies. To address the above issue, we propose a new image restoration model, Cross Aggregation Transformer (CAT). The core of our CAT is the Rectangle-Window Self-Attention (Rwin-SA), which utilizes horizontal and vertical rectangle window attention in different heads parallelly to expand the attention area and aggregate the features cross different windows. We also introduce the Axial-Shift operation for different window interactions. Furthermore, we propose the Locality Complementary Module to complement the self-attention mechanism, which incorporates the inductive bias of CNN (e.g., translation invariance and locality) into Transformer, enabling global-local coupling. Extensive experiments demonstrate that our CAT outperforms recent state-of-the-art methods on several image restoration applications. The code and models are available at https://github.com/zhengchen1999/CAT.Comment: Accepted to NeurIPS 2022. Code is available at https://github.com/zhengchen1999/CA

FreeDrag: Feature Dragging for Reliable Point-based Image Editing

To serve the intricate and varied demands of image editing, precise and flexible manipulation in image content is indispensable. Recently, Drag-based editing methods have gained impressive performance. However, these methods predominantly center on point dragging, resulting in two noteworthy drawbacks, namely "miss tracking", where difficulties arise in accurately tracking the predetermined handle points, and "ambiguous tracking", where tracked points are potentially positioned in wrong regions that closely resemble the handle points. To address the above issues, we propose FreeDrag, a feature dragging methodology designed to free the burden on point tracking. The FreeDrag incorporates two key designs, i.e., template feature via adaptive updating and line search with backtracking, the former improves the stability against drastic content change by elaborately controls feature updating scale after each dragging, while the latter alleviates the misguidance from similar points by actively restricting the search area in a line. These two technologies together contribute to a more stable semantic dragging with higher efficiency. Comprehensive experimental results substantiate that our approach significantly outperforms pre-existing methodologies, offering reliable point-based editing even in various complex scenarios.Comment: 13 pages, 14 figure

Does rDLPFC activity alter trust? Evidence from a tDCS study

Trust plays an important role in the human economy and people’s social lives. Trust is affected by various factors and is related to many brain regions, such as the dorsolateral prefrontal cortex (DLPFC). However, few studies have focused on the impact of the DLPFC on trust through transcranial direct current stimulation (tDCS), although abundant psychology and neuroscience studies have theoretically discussed the possible link between DLPFC activity and trust. In the present study, we aimed to provide evidence of a causal relationship between the rDLPFC and trust behavior by conducting multiple rounds of the classical trust game and applying tDCS over the rDLPFC. We found that overall, anodal stimulation increased trust compared with cathodal stimulation and sham stimulation, while the results in different stages were not completely the same. Our work indicates a causal relationship between rDLPFC excitability and trust behavior and provides a new direction for future research

Hierarchical Integration Diffusion Model for Realistic Image Deblurring

Diffusion models (DMs) have recently been introduced in image deblurring and exhibited promising performance, particularly in terms of details reconstruction. However, the diffusion model requires a large number of inference iterations to recover the clean image from pure Gaussian noise, which consumes massive computational resources. Moreover, the distribution synthesized by the diffusion model is often misaligned with the target results, leading to restrictions in distortion-based metrics. To address the above issues, we propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Specifically, we perform the DM in a highly compacted latent space to generate the prior feature for the deblurring process. The deblurring process is implemented by a regression-based method to obtain better distortion accuracy. Meanwhile, the highly compact latent space ensures the efficiency of the DM. Furthermore, we design the hierarchical integration module to fuse the prior into the regression-based model from multiple scales, enabling better generalization in complex blurry scenarios. Comprehensive experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods. Code and trained models are available at https://github.com/zhengchen1999/HI-Diff.Comment: Code is available at https://github.com/zhengchen1999/HI-Dif

Rheological properties and structural features of coconut milk emulsions stabilized with maize kernels and starch

peer-reviewedIn this study, maize kernels and starch with different amylose contents at the same concentration were added to coconut milk. The nonionic composite surfactants were used to prepare various types of coconut milk beverages with optimal stability, and their fluid properties were studied. The steady and dynamic rheological property tests show that the loss modulus (G″) of coconut milk is larger than the storage modulus (G′), which is suitable for the pseudoplastic fluid model and has a shear thinning effect. As the droplet size of the coconut milk fluid changed by the addition of maize kernels and starch, the color intensity, ζ-potential, interfacial tension and stability of the sample significantly improved. The addition of the maize kernels significantly reduced the size of the droplets (p < 0.05). The potential values of zeta (ζ) and the surface tension of the coconut milk increased. Based on the differential scanning calorimetry (DSC) measurement, the addition of maize kernels leads to an increase in the transition temperature, especially in samples with a high amylose content. The higher transition temperature can be attributed to the formation of some starches and lipids and the partial denaturation of proteins in coconut milk, but phase separation occurs. These results may be helpful for determining the properties of maize kernels in food-containing emulsions (such as sauces, condiments, and beverages) that achieve the goal of physical stability

Imaging Molecular Outflow in Massive Star-forming Regions with HNCO Lines

Protostellar outflows are considered a signpost of star formation. These outflows can cause shocks in the molecular gas and are typically traced by the line wings of certain molecules. HNCO (4--3) has been regarded as a shock tracer because of the high abundance in shocked regions. Here we present the first imaging results of HNCO (4--3) line wings toward nine sources in a sample of twenty three massive star-forming regions using the IRAM 30\,m telescope. We adopt the velocity range of the full width of HC$_{3}$N (10--9) and H$^{13}$CO$^+$ (1--0) emissions as the central emission values, beyond which the emission from HNCO (4--3) is considered to be from line wings. The spatial distributions of the red- and/or blue-lobes of HNCO (4--3) emission nicely associate with those lobes of HCO$^{+}$ (1--0) in most of the sources. High intensity ratios of HNCO (4--3) to HCO$^+$ (1--0) are obtained in the line wings. The derived column density ratios of HNCO to HCO$^+$ are consistent with those previously observed towards massive star-forming regions. These results provide direct evidence that HNCO could trace outflow in massive star-forming regions. This work also implies that the formation of some HNCO molecules is related to shock, either on the grain surface or within the shocked gas.Comment: 18 pages, 4 tables, 4 figures, and accepted for publication in Ap

Effects of oligosaccharides on particle structure, pasting and thermal properties of wheat starch granules under different freezing temperatures

peer-reviewedThe effects of fructooligosaccharides (FOS), galactooligosaccharides (GOS), and xylooligosaccharides (XOS) on gelatinization, retrogradation, thermal properties and particle size of wheat starch at different freezing temperatures were studied. The results showed that the wheat starch porosity, particle size, peak viscosity increased with increasing freezing temperature. With the addition of 16% oligosaccharides to starch, the porosity, particle size, crystallinity, initial gelatinization temperature, peak value, breakdown and retrogradation viscosity of the starch granules significantly decreased in the order of XOS > GOS > FOS. However, the pasting temperature of the granules increased. The addition of oligosaccharides (especially XOS, which has the most significant effect in inhibiting starch retrogradation) can inhibit the formation of starch crystal structures to a certain extent, reduce the damage from ice crystals to starch granules and delay starch retrogradation. Therefore, functional oligosaccharides can be used as a potentially effective additive to increase freezing stability in frozen starch-based foods