lem7,a Novel Temperature-SensitiveArabidopsisMutation That Reversibly Inhibits Vegetative Development

AbstractAn important question in developmental biology concerns the mechanisms by which a few cells coordinate division and differentiation to yield the complex structures and organs found in multicellular organisms. During vegetative growth in plants, cells in the apical meristem must coordinate division and differentiation to yield the fully mature leaf organ. Alterations in these processes may result in an abnormal leaf. In this paper we present the isolation and characterization of an EMS-generated, cold-temperature-sensitive mutation inArabidopsis thaliana,designatedlem7(leafmorphogenesis).lem7is a semidominant mutation that maps to a novel locus on chromosome 2. When grown at 16°C,lem7reversibly arrests leaf development at the shoot apex. In contrast,lem7grown at 30°C appears phenotypically normal. Our data also suggest that theLem7locus may not be involved solely in leaf organogenesis, but may also play a role in floral development and the maintenance of patterns and structures after cellular differentiation. At an intermediate temperature of 23°C, leaves on thelem7plant emerged phenotypically normal but began to show drastic changes at about 13 days postgermination. These changes include a reduced bilateral symmetry, a rough leaf lamina, a reduced number of trichomes, and an altered vascular network. Leaves that developed at the permissive temperature (30°C) and shifted to the nonpermissive temperature (16°C) form tumor-like outgrowths. Histological analysis of these tumor-like outgrowths and leaves grown at the intermediate temperature reveal abnormally large mesophyll cells, a disorganized mesophyll layer, and collapsed epidermal cells. We propose that the reversible inhibition of leaf development inlem7under nonpermissive temperatures may serve as a useful tool for identifying genes involved inArabidopsisleaf organogenesis

Why People Search for Images using Web Search Engines

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1)Why do people search for images in text-based Web image search systems? (2)How does image search behavior change with user intent? (3)Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling

Reviews on Mechanisms of In Vitro

It is widely acknowledged that the excessive reactive oxygen species (ROS) or reactive nitrogen species (RNS) induced oxidative stress will cause significant damage to cell structure and biomolecular function, directly or indirectly leading to a number of diseases. The overproduction of ROS/RNS will be balanced by nonenzymatic antioxidants and antioxidant enzymes. Polysaccharide or glycoconjugates derived from natural products are of considerable interest from the viewpoint of potent in vivo and in vitro antioxidant activities recently. Particularly, with regard to the in vitro antioxidant systems, polysaccharides are considered as effective free radical scavenger, reducing agent, and ferrous chelator in most of the reports. However, the underlying mechanisms of these antioxidant actions have not been illustrated systematically and sometimes controversial results appeared among various literatures. To address this issue, we summarized the latest discoveries and advancements in the study of antioxidative polysaccharides and gave a detailed description of the possible mechanisms

Controlled biomineralization of electrospun poly(ε-caprolactone) fibers for enhancing their mechanical properties

Electrospun polymeric fibers have been investigated as scaffolding materials for bone tissue engineering. However, their mechanical properties, and in particular stiffness and ultimate tensile strength, cannot match those of natural bones. The objective of the study was to develop novel composite nanofiber scaffolds by attaching minerals to polymeric fibers using an adhesive material-the mussel-inspired protein polydopamine-as a superglue . Herein, we report for the first time the use of dopamine to regulate mineralization of electrospun poly(ε-caprolactone) (PCL) fibers to enhance their mechanical properties. We examined the mineralization of the PCL fibers by adjusting the concentration of HCO3 - and dopamine in the mineralized solution, the reaction time and the surface composition of the fibers. We also examined mineralization on the surface of polydopamine-coated PCL fibers. We demonstrated the control of morphology, grain size and thickness of minerals deposited on the surface of electrospun fibers. The obtained mineral coatings render electrospun fibers with much higher stiffness, ultimate tensile strength and toughness, which could be closer to the mechanical properties of natural bone. Such great enhancement of mechanical properties for electrospun fibers through mussel protein-mediated mineralization has not been seen previously. This study could also be extended to the fabrication of other composite materials to better bridge the interfaces between organic and inorganic phases

Constructing an Interaction Behavior Model for Web Image Search

User interaction behavior is a valuable source of implicit relevance feedback. In Web image search a different type of search result presentation is used than in general Web search, which leads to different interaction mechanisms and user behavior. For example, image search results are self-contained, so that users do not need to click the results to view the landing page as in general Web search, which generates sparse click data. Also, two-dimensional result placement instead of a linear result list makes browsing behaviors more complex. Thus, it is hard to apply standard user behavior models (e.g., click models) developed for general Web search to Web image search. In this paper, we conduct a comprehensive image search user behavior analysis using data from a lab-based user study as well as data from a commercial search log. We then propose a novel interaction behavior model, called grid-based user browsing model (GUBM), whose design is motivated by observations from our data analysis. GUBM can both capture users' interaction behavior, including cursor hovering, and alleviate position bias. The advantages of GUBM are two-fold: (1) It is based on an unsupervised learning method and does not need manually annotated data for training. (2) It is based on user interaction features on search engine result pages (SERPs) and is easily transferable to other scenarios that have a grid-based interface such as video search engines. We conduct extensive experiments to test the performance of our model using a large-scale commercial image search log. Experimental results show that in terms of behavior prediction (perplexity), and topical relevance and image quality (normalized discounted cumulative gain (NDCG)), GUBM outperforms state-of-the-art baseline models as well as the original ranking. We make the implementation of GUBM and related datasets publicly available for future studies.Comment: 10 page

Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models

A retrieval model should not only interpolate the training data but also extrapolate well to the queries that are different from the training data. While neural retrieval models have demonstrated impressive performance on ad-hoc search benchmarks, we still know little about how they perform in terms of interpolation and extrapolation. In this paper, we demonstrate the importance of separately evaluating the two capabilities of neural retrieval models. Firstly, we examine existing ad-hoc search benchmarks from the two perspectives. We investigate the distribution of training and test data and find a considerable overlap in query entities, query intent, and relevance labels. This finding implies that the evaluation on these test sets is biased toward interpolation and cannot accurately reflect the extrapolation capacity. Secondly, we propose a novel evaluation protocol to separately evaluate the interpolation and extrapolation performance on existing benchmark datasets. It resamples the training and test data based on query similarity and utilizes the resampled dataset for training and evaluation. Finally, we leverage the proposed evaluation protocol to comprehensively revisit a number of widely-adopted neural retrieval models. Results show models perform differently when moving from interpolation to extrapolation. For example, representation-based retrieval models perform almost as well as interaction-based retrieval models in terms of interpolation but not extrapolation. Therefore, it is necessary to separately evaluate both interpolation and extrapolation performance and the proposed resampling method serves as a simple yet effective evaluation tool for future IR studies.Comment: CIKM 2022 Full Pape

Structural characterization of an α-1, 6-linked galactomannan from natural Cordyceps 2 sinensis

An α-1, 6-linked galactomannan was isolated and purified from natural Cordyceps sinensis. The fine structure analysis of this polysaccharide was elucidated based on partial acid hydrolysis, monosaccharide composition, methylation and 1D/2D nuclear magnetic resonance (NMR) spectroscopy. Monosaccharide composition analysis revealed that this polysaccharide was mainly composed of galactose (68.65%), glucose (6.65%) and mannose (24.02%). However, after partial acid hydrolysis the percentages of galactose, glucose and mannose were changed to 3.96%, 13.82% and 82.22%, respectively. The molecular weight of this polysaccharide was 7207. Methylation and NMR analysis revealed that this galactomannan had a highly branched structure, mainly consisted of a mannan skeleton and galactofuranosyl chains. The structure of galactofuranosyl part was formed by alternating (1 → 5)-lined β-Galf and (1 → 6)-liked β-Galf or a single (1 → 6)-liked β-Galf, attaching to the O-2 and O-4 of the mannose chain, and terminated at β-T-Galf. The mannan core was revealed by analyzing the partial acid hydrolysate of the galactomannan and the structure was composed of (1 → 6)-linked α-Manp backbone, with substituted at C-2 by short chains of 2-substituted Manp or Galf branches

Why people search for images using web search engines

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1) Why do people search for images in text-based Web image search systems? (2) How does image search behavior

Effects of Differently Processed Carrots on Ulcerative Colitis in Mice

The incidence of ulcerative colitis (UC) has been increasing in recent years. Due to the limitations of traditional drug therapies for UC, natural foods that can prevent this disease and alleviate its symptoms are becoming a research hot topic, but the effects of processing methods on their activity remain unknown. Therefore, the effects of three different processing methods (pulping, high-temperature cooking, and fermentation) on carotenoid and dietary fiber contents as well as carotenoid bioaccessibility in carrots were explored in this study. C57BL/6J mice were used to create a mouse model of UC induced by dextran sulfate sodium (DSS) and the mice received dietary intervention with freeze dried powder of carrots (5.05%, on a dry mass basis) for 14 days. Body mass change, disease activity index (DAI) and colon parameters (length, pathology, inflammatory factors, oxidative stress level, goblet cell number, tight junction protein expression, and short-chain fatty acid content) were analyzed to evaluate the effects of three differently processed carrots on UC. The results showed compared with pulping, high-temperature cooking and fermentation significantly decreased the soluble, insoluble and total dietary fiber contents of carrots (P < 0.05), increased the bioaccessibility of carotenoids (P < 0.05), while fermentation significantly increased the proportion of soluble dietary fiber in total dietary fiber (P < 0.05). Compared with the model group, all processed carrots could significantly inhibit the change of body mass loss and DAI (P < 0.05), decrease the levels of tumor necrosis factor-α (TNF-α), interleukin (IL)-1β and IL-6 (P < 0.05), increase the level of IL-10 (P < 0.05), and up-regulate the expression of tight junction proteins (ZO-1, claudin-1, and occludin) (P < 0.05). High-temperature cooked or fermented carrots could significantly alleviate colon shortening (P < 0.05), and relieve the pathological damage of colon tissue (P < 0.05). Meanwhile, fermented carrots could significantly inhibit the production of malondialdehyde (MDA) (P < 0.05), improve the decrease in the number of goblet cells (P < 0.05), increase the level of butyric acid (P < 0.05) and possess the best inhibitory effect on IL-6 production. In summary, differently processed carrots could ameliorate ulcerative colitis to different extends, the most pronounced effect being observed with fermented carrots