Absence Of Labels Research Articles

Nonlinear optical microscopy for label-free detection of gastrointestinal neuroendocrine tumors.

Neuroendocrine tumors (NETs), which are rare and slow-growing neoplasms, pose a diagnostic challenge as they are clinically silent at the time of presentation. Here, gastrointestinal neuroendocrine tumors were researched by nonlinear microscopy, and results demonstrate that this technique has the capability to identify neuroendocrine tumors in the absence of labels and can, in particular, detect rare neuroendocrine tumor cells, vascular invasion, desmoplastic reaction, and fibroelastosis induced by neuroendocrine tumors. These conclusions highlight the possibility of nonlinear optical microscopy as a diagnostic tool for label-freely differentiating neuroendocrine tumors by these histopathologic features.

Categorization in infancy: labeling induces a persisting focus on commonalities.

Recent studies with infants and adults demonstrate a facilitative role of labels in object categorization. A common interpretation is that labels highlight commonalities between objects. However, direct evidence for such a mechanism is lacking. Using a novel object category with spatially separate features that are either of low or high variability across the stimulus set, we tracked 12‐month‐olds’ attention to object features during learning and at test. Learning occurred in both conditions, but what was learned depended on whether or not labels were heard. A detailed analysis of eye movements revealed that infants in the two conditions employed different object processing strategies. In the silent condition, looking patterns were governed exclusively by the variability of object parts. In the label condition, infants’ categorization performance was linked to their relative attention to commonalities. Moreover, the commonality focus persisted after learning even in the absence of labels. These findings constitute the first experimental evidence that labels induce a persistent focus on commonalities.

Assessment of colloid response by nonlinear optical microscopy after preoperative radiochemotherapy for rectal carcinoma.

Colloid response is a type of tumor response that occurs after preoperative radiochemotherapy for rectal carcinoma. Given its important influence on survival, the colloid response should be considered when estimating histopathological reactions. Here, multiphoton microscopy (MPM) was applied to evaluate the colloid response ex vivo. This study demonstrated that MPM has the capability to visualize the colloid response in the absence of labels and can, in particular, identify rare residual carcinomatous cells in mucin pools. These results highlight the potential of this nonlinear optical technique as a diagnostic tool for tumor response after neoadjuvant treatment.

A statistical design approach to unsupervised codeword selection in image retrieval

Recently, the bag-of-features (BoF) representation has attracted substantial interest in large scale image retrieval. In BoF representation, images are treated as collections of local invariant descriptors. A visual codebook is constructed by quantizing the local descriptors into clusters, whose centers are called codewords. Then each image could be represented by the frequency histogram over the codebook, and existing text retrieval techniques such as inverted indexing could be applied to image retrieval. It has been recognized that the quality of the codebook is critical to the performance of BoF-based image retrieval systems, and codeword selection is therefore a fundamental problem. Many of the existing approaches for codeword selection make use of the label information of the images. However, collecting the labels of a large amount of images is very expensive. In this paper, we investigate the problem of codeword selection in the absence of labels. Inspired from the techniques of statistical design, we propose two novel unsupervised learning algorithms to select the codewords for an image retrieval system. Specifically, we assume that the relationship between the relevance score and the BoF representation of an image could be expressed by a linear regression model, and select the codewords which can improve the regression model the most. In other words, if the selected codewords are used to train the regression model, the expected prediction error can be minimized. Extensive experimental results have demonstrated the effectiveness of our proposed methods.

N‐Terminal Labeling of Peptides by Trypsin‐Catalyzed Ligation for Quantitative Proteomics

Labeling tryptic peptides with stable isotopes is one of the most important methods for quantitative proteomics, and many ingenious chemical labeling strategies have been developed. Incorporation of one isotopically labeled tag onto a peptide terminus represents an ideal labeling approach, as it would simplify the interpretation of mass spectra. Moreover, the absence of labels on the side chains would facilitate the quantification of post-translational modifications (PTMs). However, to date all the reported chemical labeling strategies, including dimethyl labeling, iTRAQ (isobaric tags for absolute and relative quantification), and ICAT (isotope-coded affinity tags), result in the modification of side chains. One promising method to achieve the incorporation of a single tag is enzymatic labeling. Proteolytic O labeling can specifically label tryptic peptide termini without modification of side chains, but the small change in mass and O/O back-exchange, i.e. the exchange of O with O after the labeling process hinder its wide application in quantitative proteomics. Herein, we report a novel enzymatic labeling approach, in which trypsin is used as a ligase to specifically incorporate amino acids labeled with stable isotopes onto the N termini of peptides for quantitative analysis. Trypsin is a serine protease that specifically hydrolyzes peptide bonds in proteins after arginine and lysine residues. However, trypsin also catalyses peptide synthesis in organic solvents. Therefore, it is possible to covalently link isotopically labeled amino acids to tryptic peptides by using trypsin as a ligase. In this study, arginine, the prototype substrate for trypsin, was used as an acyl moiety donor. The primary amine group of arginine was protected with a benzoyl group (Bz) to prevent the formation of dipeptides or oligopeptides, and the carboxy group was esterified with ethanol to activate the acyl donor (see the Supporting Information, Figure S1). The final product, Na-benzoyl-l-arginine ethyl ester (Bz–R–OEt), was used as a substrate for the trypsin-catalyzed ligation. The quantitative proteomics workflow based on the trypsincatalyzed N-terminal labeling is shown in Figure 1a. Trypsin was first used as a protease to digest proteins in an aqueous solution (1m urea/50 mm Tris-HCl, pH 8.0). Then, the generated tryptic peptides were lyophilized and transferred to an ethanol solution containing 4% aqueous buffer (0.1m TrisHCl, pH 8.0). Next, trypsin immobilized on magnetic nanoparticles (IM-trypsin) and Bz–R–OEt were added to the ethanol solution for the N-terminal labeling of peptides with Bz–R based on a kinetically controlled mechanism (see the Supporting Information). Finally, quantification of the proteins was achieved by the differential labeling of two samples by using Bz–R–OEt (light label) and Bz–(C6)R–OEt (bearing six C atoms; heavy label), the incorporation of which are indicated by mass shifts of 260 and 266 Da, respectively. We first validated the protease and ligase activities of trypsin by using the synthetic peptide VGKANEELAGVVAEVQK (Figure 1b; m/z= 1740.86), which contains one trypsin cleavage site. In aqueous solution, treatment with IMtrypsin generated a shorter peptide ANEELAGVVAEVQK (Figure 1b, m/z= 1456.82). Treatment of the obtained peptide with an ethanol solution containing Bz–R–OEt and IMtrypsin gave an N-terminus labeled peptide Bz–RANEELAGVVAEVQK (Figure 1b, m/z= 1716.9). Similar results were also obtained for three other synthetic peptides that contained one trypsin cleavage site (Supporting Information, Figure S2). These examples clearly illustrate that trypsin functions as a protease in an aqueous solution whereas it acts as an N-terminus ligase in an ethanol solution. Thus, these results imply that this enzymatic labeling approach can be used to label peptides generated by trypsin digestion of a proteome sample. In this study, free trypsin was used for the digestion of proteins, as is conventional in proteomics analysis, whereas IM-trypsin (trypsin from the same source) was used for ligation because it is more tolerant to organic solvents and it is readily removed using magnetism. We tested whether the labeling of side-chain primary amino groups would also be facilitated by trypsin ligase. A synthetic peptide VIFIEHAKRKG, containing two sidechain amino groups and one terminal primary amino group, was labeled by trypsin. The Arg tag was incorporated only onto the terminal primary amino group and not onto the sidechain amino groups (see the Supporting Information, Figure S3a). These results are markedly different from those of other amine labeling approaches. For example, for labeling [*] Y. Pan, Prof. Dr. M. Ye, Dr. L. Zhao, K. Cheng, M. Dong, C. Song, H. Qin, Dr. F. Wang, Prof. Dr. H. Zou CAS Key Lab of Separation Sciences for Analytical Chemistry National Chromatographic R&A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 (China) E-mail: mingliang@dicp.ac.cn hanfazou@dicp.ac.cn

Why Waldo befriended the dummy? k-Anonymization of social networks with pseudo-nodes

For a graph-based representation of a social network, the identity of participants can be uniquely determined if an adversary has background structural knowledge about the graph. We focus on degree-based attacks, wherein the adversary knows the degrees of particular target vertices and we aim to protect the anonymity of participants through k-anonymization, which ensures that every participant is equivalent to at least k − 1 other participants with respect to degree. We introduce a natural and novel approach of introducing “dummy” participants into the network and linking them to each other and to real participants in order to achieve this anonymity. The advantage of our approach lies in the nature of the results that we derive. We show that if participants have labels associated with them, the problem of anonymizing a subset of participants is NP-Complete. On the other hand, in the absence of labels, we give an $$\mathcal{O}(nk)$$ algorithm to optimally k-anonymize a subset of participants or to near-optimally k-anonymize all real and all dummy participants. For degree-based-attacks, such theoretical guarantees are novel.

Effects of categorical labels on similarity judgments: a critical analysis of similarity-based approaches.

Our goal in the present study was to evaluate the claim that category labels affect children's judgments of visual similarity. We presented preschool children with discriminable and identical sets of animal pictures and asked them to make perceptual judgments in the presence or absence of labels. Our findings indicate that children who are asked to make perceptual judgments about identical items judge discriminable items less accurately when making subsequent similarity judgments. Thus, labels do not generally affect children's perceptual similarity judgments; rather, children's reliance on labels to make similarity judgments appears to be attributable to flaws in the methodological approaches used in prior studies. These results have implications for the role of perceptual and conceptual information in children's categorization and induction.

Image retrieval based on incremental subspace learning

Many problems in information processing involve some form of dimensionality reduction, such as face recognition, image/text retrieval, data visualization, etc. The typical linear dimensionality reduction algorithms include principal component analysis (PCA), random projection, locality-preserving projection (LPP), etc. These techniques are generally unsupervised which allows them to model data in the absence of labels or categories. In this paper, we propose a semi-supervised subspace learning algorithm for image retrieval. In relevance feedback-driven image retrieval system, the user-provided information can be used to better describe the intrinsic semantic relationships between images. Our algorithm is fundamentally based on LPP which can incorporate user's relevance feedbacks. As the user's feedbacks are accumulated, we can ultimately obtain a semantic subspace in which different semantic classes can be best separated and the retrieval performance can be enhanced. We compared our proposed algorithm to PCA and the standard LPP. Experimental results on a large collection of images have shown the effectiveness and efficiency of our proposed algorithm.

A study of resonance electron capture ionization on a quadrupole tandem mass spectrometer

Procedures that allow the realization of resonance electron capture (REC) mode on a commercial triple-quadrupole mass spectrometer, after some simple modifications, are described. REC mass spectrometry (MS) and tandem mass spectrometry (MS/MS) experiments were performed and spectra for some compounds were recorded. In particular, the charge-remote fragmentation (CRF) spectra of [M − H]− ions of docosanoic and docosenoic acids under low-energy collisionally activated dissociation (CAD) conditions were obtained, and showed that there were no significant differences for [M − H]− ions produced at different resonances (i.e. for [M − H]− ions with different structures). This observation was explained on the basis of results obtained from deuterium-labeled fatty acids, which showed that different CRF ions (but with the same m/z value in the absence of labels) could be produced by different mechanisms, and all of them were obviously realized under CAD conditions that made spectra practically indistinguishable. The other example, which compared the REC-MS/MS spectrum of [M − H]− ions and EI-MS/MS spectrum of M+· ions of daidzein, demonstrated the potential of the REC-MS/MS technique for more complex structure elucidation. Copyright © 2000 John Wiley & Sons, Ltd.

A study of resonance electron capture ionization on a quadrupole tandem mass spectrometer.

Procedures that allow the realization of resonance electron capture (REC) mode on a commercial triple-quadrupole mass spectrometer, after some simple modifications, are described. REC mass spectrometry (MS) and tandem mass spectrometry (MS/MS) experiments were performed and spectra for some compounds were recorded. In particular, the charge-remote fragmentation (CRF) spectra of [M - H](-) ions of docosanoic and docosenoic acids under low-energy collisionally activated dissociation (CAD) conditions were obtained, and showed that there were no significant differences for [M - H](-) ions produced at different resonances (i.e. for [M - H](-) ions with different structures). This observation was explained on the basis of results obtained from deuterium-labeled fatty acids, which showed that different CRF ions (but with the same m/z value in the absence of labels) could be produced by different mechanisms, and all of them were obviously realized under CAD conditions that made spectra practically indistinguishable. The other example, which compared the REC-MS/MS spectrum of [M - H](-) ions and EI-MS/MS spectrum of M(+.) ions of daidzein, demonstrated the potential of the REC-MS/MS technique for more complex structure elucidation.