A Label-Free Electrochemical Immunosensor for CEA Detection on a Novel Signal Amplification Platform of Cu2S/Pd/CuO Nanocomposites

Carcinoembryonic antigen (CEA) is regarded as one of the crucial tumor markers for colorectal cancer. In this study, we developed the snowflake Cu2S/Pd/CuO nanocomposite to construct an original label-free electrochemical immunosensor for the ultrasensitive detection of CEA levels. The nanocomposite of cuprous sulfide (Cu2S) with Pd nanoparticles (Pd NPs) was synthesized through an in situ formation of Pd NPs on the Cu2S. Cuprous sulfide (Cu2S) and CuO can not only be used as a carrier to increase the reaction area but also catalyze the substrate to generate current signal. Palladium nanoparticles (Pd NPs) have excellent catalytic properties and good biocompatibility, as well as the ability of excellent electron transfer. The immunosensor was designed using 5 mmol/L H2O2 as the active substrate by optimizing the conditions with a detection range from 100 fg/ml to 100 ng/ml and a minimum detection limit of 33.11 fg/ml. The human serum was detected by electrochemical immunoassay, and the results were consistent with those of the commercial electrochemical immunosensor. Therefore, the electrochemical immunosensor can be used for the detection of human serum samples and have potential value for clinical application.

Hierarchical Encoder-Decoder With Soft Label-Decomposition for Mitochondria Segmentation in EM Images

Semantic segmentation of mitochondria from electron microscopy (EM) images is an essential step to obtain reliable morphological statistics about mitochondria. However, automatically delineating plenty of mitochondria of varied shapes from complex backgrounds with sufficient accuracy is challenging. To address these challenges, we develop a hierarchical encoder-decoder network (HED-Net), which has a three-level nested U-shape architecture to capture rich contextual information. Given the irregular shape of mitochondria, we introduce a novel soft label-decomposition strategy to exploit shape knowledge in manual labels. Rather than simply using the ground truth label maps as the unique supervision in the model training, we introduce additional subcategory-aware supervision by softly decomposing each manual label map into two complementary label maps according to mitochondria's ovality. The three label maps are integrated with our HED-Net to supervise the model training. While the original label map guides the network to segment all the mitochondria of varied shapes, the auxiliary label maps guide the network to segment subcategories of mitochondria of circular shape and elliptic shape, respectively, which are much more manageable tasks. Extensive experiments on two public benchmarks show that our HED-Net performs favorably against state-of-the-art methods.

Consumer Comprehension of the Nutrition Facts Label: A Comparison of the Original and Updated Labels

Purpose: Compare consumers’ overall label comprehension of the original Nutrition Facts (NF) label with the updated label. Design: Online survey conducted in 2019. Participants randomly assigned to original label, updated-single column, or updated-dual column labeling condition and asked to complete a series of label comprehension questions. Setting: Online survey; participants recruited through Prolific. Sample: N = 992 U.S. adults. Sample similar to U.S. population in terms of sex (49.2% female), race (73.3% White/Caucasian), and household size (mean = 2.7 members). However, sample was younger (median age: 29.0), more educated (98.8% high school graduate or higher), and exhibited a lower rate of obesity (22.6% obese) than the U.S. population. Measures: Dependent variables: objective (% correct) NF label comprehension. Independent variables: label condition, nutrition knowledge, and socio-demographic variables. Analysis: Regression analysis assessed relationships between label condition and label comprehension. Significance level of 5% used for analyses. Results: Average score for objective comprehension was 81.4%. The updates did not significantly improve label comprehension. Participants in the updated NF label conditions had trouble answering questions related to total and added sugars. Conclusions: Results suggest consumers may struggle to correctly utilize information on the updated NF label, specifically total and added sugars. Consumers may benefit from educational opportunities on using the new label.

Bryophytes Occurrences Dataset Based On SYKO Herbarium Moss Collection

The dataset with 49,726 bryophytes occurrences (49,261 moss occurrences and 465 liverworts occurrences), located predominantly on the territory European north-east Russia, is described in this data paper. The dataset was based on the digitised moss labels from the Institute of Biology of Komi Scientific Сenter of the Ural Branch of the Russian Academy of Sciences herbarium (SYKO). The information from the labels was recognised, cleaned and brought into compliance with the Darwin Core. More than 99.9% of occurrences were georeferenced with a precision of at least 3 km. For each occurrence, the original label image URL was given. The dataset contains occurrences of 539 moss and liverworts taxa (species and lower ranks) belonging to 190 genera and 75 families. Information about 49,726 bryophytes occurrences was published in GBIF. The dataset was based on label data of 94% of SYKO herbarium moss collection specimens. Most of the occurrences were described with the following fields: occurrenceID, institutionID, collectionCode, catalogNumber, basisOfRecord, scientificName, taxonRank, kingdom, phylum, class, order, family, genus, recordedBy, identifiedBy, associatedMedia, day, month, year, country, countryCode, decimalLatitude, decimalLongitude, geodeticDatum, coordinateUncertaintyInMetres, georeferencedBy.

Partial Multi-Label Learning via Multi-Subspace Representation

Partial Multi-Label Learning (PML) aims to learn from the training data where each instance is associated with a set of candidate labels, among which only a part of them are relevant. Existing PML methods mainly focus on label disambiguation, while they lack the consideration of noise in the feature space. To tackle the problem, we propose a novel framework named partial multi-label learning via MUlti-SubspacE Representation (MUSER), where the redundant labels together with noisy features are jointly taken into consideration during the training process. Specifically, we first decompose the original label space into a latent label subspace and a label correlation matrix to reduce the negative effects of redundant labels, then we utilize the correlations among features to project the original noisy feature space to a feature subspace to resist the noisy feature information. Afterwards, we introduce a graph Laplacian regularization to constrain the label subspace to keep intrinsic structure among features and impose an orthogonality constraint on the correlations among features to guarantee discriminability of the feature subspace. Extensive experiments conducted on various datasets demonstrate the superiority of our proposed method.

Hoax Detection at Social Media With Text Mining Clarification System-Based

<p style="text-indent: 0.36cm; margin-top: 0.04cm; margin-bottom: 0cm; line-height: 100%;" align="justify"><em>Hoax is a current issue that is troubling the public and causes riot in various fields, ranging from politics, culture, security and order, to economics. This problem cannot be separated from the impact of rapid use of social media. As a result, every day there are thousands of information spread on social media, which is not necessarily valid, so that people are potentially exposed to hoax on social media. The hoax detection system in this study was designed with an Unsupervised Learning approach so that it did not require data training. The system is built using the Text Rank algorithm for keyword extraction and the Cosine Similarity algorithm to calculate the level of document similarity. The keyword extraction results will be used to search for content related to input from users using the search engine, then calculate the similarity value. If the related content tends to come from trusted media, then the content is potentially factual. Likewise, if the related content tends to be published by unreliable media, then there is the potential for hoax. The hoax detection system has been tested using confusion matrix, from 20 news content data consisting of 10 correct issues and 10 wrong issues. Then the system produces a classification with details of 13 issues including wrong and 7 issues including true, then the number of classifications that match the original label are 15 issues. Based on the results of the classification, an accuracy value of 75% was obtained.</em></p>

Broadening Participation in an Increasingly Digitized World

Participation within digitized collections has shown boom, but diversity of participants has remained static. Traditionally, natural history collections were only utilized by researchers with access to the physical collections. With the advent of open source digitized specimens, whether through transcription of the original label onto an electronic database, sound bites, two-dimensional photographs, or three-dimensional volume files, natural history collections are now at nearly everyone’s fingertips. Although collections have been historically clustered in the northern hemisphere, preliminary data suggest that researchers from the southern hemisphere have started using collections more via online portals. Studies have shown that a more heterogeneous community leads to an increase in the quality of science and publications. iDigBio (Integrated Digitized Biocollections), the United States’ national resource for Advancing Digitization of Biodiversity Collections (ADBC), is a National Science Foundation (NSF) funded initiative that makes millions of biological specimens, in the form of data and images, available electronically to the wider world. Our network of institutions across the world provide the digitized content that makes up our search portal. Minority serving institutions (MSIs) are an important resource for under served communities in the United States. They provide the educational and social skills required to overcome discrimination and economic disparities that these communities often face. Here, we focus on the types of institutions involved in uploading data, specifically those that identify as MSIs and the role they play in the field. After assessing MSI participation with the ADBC program by comparing databases of participants, I found that out of the nearly 400 individual institutions that contribute to the database, one-third of them identify as an MSI. The next step is further engaging contributing MSIs and identifying MSIs with natural history collections that are not a part of the iDigBio network and inviting them to join. By incorporating them into our network, we hope to reach underserved populations of students while broadening the scope of collections available. Including MSIs into our greater community of partners is not enough. We are striving to provide a greater understanding of how the iDigBio portal is used by new communities in the US with limited resources. In this way, we can provide educators with the tools necessary to better prepare their students for Science, Technology, Engineering, and Mathematics (STEM) careers, as well as improving the collections available to the world.

LPA-CBD an improved label propagation algorithm based on community belonging degree for community detection

In order to deal with stochasticity in center node selection and instability in community detection of label propagation algorithm, this paper proposes an improved label propagation algorithm named label propagation algorithm based on community belonging degree (LPA-CBD) that employs community belonging degree to determine the number and the center of community. The general process of LPA-CBD is that the initial community is identified by the nodes with the maximum degree, and then it is optimized or expanded by community belonging degree. After getting the rough structure of network community, the remaining nodes are labeled by using label propagation algorithm. The experimental results on 10 real-world networks and three synthetic networks show that LPA-CBD achieves reasonable community number, better algorithm accuracy and higher modularity compared with other four prominent algorithms. Moreover, the proposed algorithm not only has lower algorithm complexity and higher community detection quality, but also improves the stability of the original label propagation algorithm.

Type specimens of names of Altai taxa of Boraginaceae described by C. F. Ledebour, A. A. Bunge and C. A. Meyer kept in the Herbarium of the Komarov Botanical Institute (LE)

The article contains information about 32 type specimens of the names of 14 species of the boraginaceous genera Arnebia (incl. Lithospermum cornutum Ledeb.), Lappula (= Echinospermum), Onosma, Rochelia, and Solenanthus, kept in Herbarium of the Komarov Botanical Institute (LE), and 5 specimens kept in Muséum National d’Histoire Naturelle, Paris, France (P). The type category is indicated, text of the original label and text of the protologue are cited for each specimen. Among the 37 specimens found in the collections, there are 14 lectotypes, 11 isolectotypes, 8 syntypes, 1 isosyntype and 3 authentic specimens. The lectotypes of the names of 6 taxa are designated: Arnebia guttata Bunge, Echinospermum cristatum Bunge, E. consanguineum Fisch. et C. A. Mey., Lithospermum cornutum Ledeb., Rochelia leiocarpa Ledeb., Solenanthus circinnatus Ledeb.