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Personalized recommendation has become more and more important for users to quickly find relevant items. The key issue of the recommender system is how to model user preferences. Previous work mostly employed user historical data to learn users’ preferences, but faced with the data sparsity problem. The prevalence of online social networks promotes increasing online discussion groups, and users in the same group often have similar interests and preferences. Therefore, it is necessary to integrate group information for personalized recommendation. The existing work on group-information-enhanced recommender systems mainly relies on the item information related to the group, which is not expressive enough to capture the complicated preference dependency relationships between group users and the target user. In this article, we solve the problem with the graph neural networks. Specifically, the relationship between users and items, the item preferences of groups, and the groups that users participate in are constructed as bipartite graphs, respectively, and the user preferences for items are learned end to end through the graph neural network. The experimental results on the Last.fm and Douban Movie datasets show that considering group preferences can improve the recommendation performance and demonstrate the superiority on sparse users compared

This paper examines when parties listen to interest groups and adopt their input. Interest group information can help parties bolster their positions, and by taking their input into account, parties show that they are responsive to the groups’ interests which can increase their appeal to their constituents. Listening to interest groups can, however, also repel voters who disagree with the groups’ positions. This paper argues that party and issue-level characteristics affect whether the benefits of listening to interest groups exceed the costs. Examining more than 25,000 party-interest group observations on 88 Norwegian policy proposals and using a text reuse approach to measure interest group influence, the findings indicate that public salience, party issue emphasis, interest group coalitions, and government status affect parties’ propensity to listen. This implies that interest groups can be a pertinent source of information for parties under certain circumstances which affects the link between voters and parties.

The COVID-19 pandemic brought a challenge to the health area and generated an enormous amount of information, some accurate and some not, which made it difficult to locate reliable sources of information. Scientific knowledge has become the best way to mitigate this infodemiological process. Observatories are instruments to support decision making, seeking to integrate different sources of information and communicate the results using research methodologies such as the COVID-19 Scientific Evidence Observatory. Created by members of the research group Information in Science, Technology, and Innovation in Health, of the IBICT, it aims to meet the informational demands of the most varied audiences. Its development methodology involves a knowledge management team that uses the methodological rigor of the systematic literature review to seek, evaluate, synthesize, and enable access to reliable and qualified sources of information. It provides access to different sources of national and international information from the Kaleidoscope of Science.

The present study aimed to debate a topic less addressed by most people, which involved research on a group of 20 students from rural areas, aged 10–14 years, which consists of performing two tests, namely the Ruler drop test and the Hand-eye coordination test, which aims at the reaction speed of the dominant and non-dominant hand and also the hand-eye coordination capacity of the subjects. The paper aimed to identify whether somatic factors and age influence the results of the group. In order to perform the two tests, it was necessary, for the beginning, information related to the study group, information on weight, age, height, dominant hand, respectively dominant eye. These represented the point of interest of the research, being reported individually to the test results, thus constituting the study basis of statistics. After obtaining the results, we concluded that a significant significance is encountered when comparing the dominant hand with the non-dominant one, obtaining a positive value for the dominant hand. At the same time, we interpreted after the research that females tend to have a much faster reaction speed, more significant than the males when it comes to using the non-dominant hand. The hypothesis was confirmed, with differences in somatic factors’ influence, but the others do not show significant values except those stated above. In addition to the practical part, the research involves an interesting theoretical foundation being reached aspects related to proprioception, coordination, speed, ways of using tests, and the opinion of other researchers who have conducted similar studies.

Abstract Value-directed strategic processing involves selectively attending to and processing information deemed more important while ignoring or inhibiting less important information. What we selectively attend to can be driven by the value we ascribe to the information, often based on stimulus factors such as perceptual features that make the information stand out, or conceptual features that make it easy to group information. The current study investigated whether behavioral measures of value-directed strategic processing are differentially affected when value is defined by perceptual versus conceptual features, and how normal cognitive aging impacts processing. Cognitively normal younger (N = 16; mean age: 22.1 ± 2.9 years) and older adults (N = 16; mean age: 66.9 ± 7.3 years) completed two value-directed strategic processing tasks, where value was defined by either perceptual (i.e., uppercase and lowercase letters; Letter Case task) or conceptual (i.e., animals and household items; Categories task) features. Both groups had higher recall on the Categories task compared to the Letter Case task, and higher recall for high- than low-value words. However, older adults recalled fewer total words than younger adults, but the groups did not differ across task types. These findings indicate that manipulating perceptual and/or conceptual features to define value can be used to study value-directed strategic processing in younger and older adults. Furthermore, grouping information based on conceptual features may be more effective for promoting subsequent recall in both younger and older adults.

Signal peptide is a short peptide located in the N-terminus of proteins. It plays an important role in targeting and transferring transmembrane proteins and secreted proteins to correct positions. Compared with traditional experimental methods to identify and discover signal peptides,the computational methods are faster and more efficient, which are more practical for the analysis of thousands or even millions of protein sequences in reality, especially for the metagenomic data. Therefore, computational tools are recently proposed to classify signal peptides and predict cleavage site positions, but most of them disregard the extreme data imbalance problem in these tasks. In addition, almost all these methods rely on additional group information of proteins to boost their performances, which, however, may not always be available. To deal with these issues, in this paper, we present Unbiased Organism-agnostic Signal Peptide Network(USPNet), a signal peptide prediction and cleavage site prediction model based on deep protein language model. We propose to use label distribution-aware margin (LDAM) loss and evolutionary scale modeling (ESM) embedding to handle data imbalance and object-dependence problems. Extensive experimental results demonstrate that the proposed method significantly outperforms all the previous methods on the classification performance. Additional study on the simulated metagenomic data further indicates that our model is a more universal and robust tool without dependency on additional group information of proteins, with the Matthews correlation coefficient improved by up to 17.5‰. The proposed method will be potentially useful to discover new signal peptides from the abundant metagenomic data.

According to some impression formation theories, when people perceive an individual member of a social group, the information about the group is activated more spontaneously and easily than information specific to the individual. Therefore, the judgment of individual group members might be more sensitive to group information (relatively to individuating information) the more automatic (fast, unintentional, and effortless) the judgment is. We tested this premise with a minimalistic impression formation paradigm that provided evaluative information about eight individuals and assigned them to two novel groups. In one group, three members behaved positively, and one member behaved negatively. In the other group, three members behaved negatively and one positively. In seven main experiments and 12 auxiliary experiments, we examined whether people’s automatic (but not deliberate) judgment of the atypical group members would be determined by the valence of the typical behavior in the group (group information) or the valence of the typical behaviors of that person (individuating information). Individuating information had a larger effect on automatic and deliberate evaluation than group information. The relative effect of group information (vs. individuating information) was slightly stronger on automatic than on deliberate judgment. This discrepancy increased when we increased the salience of group membership upon judgment, or when participants belonged to one of the groups. Our findings suggest that, inherently, automatic judgment of individuals is only slightly more biased than deliberate judgment by group information. Yet, under circumstances that are common in everyday life, that bias increases in automatic but not in deliberate judgment.

Abstract Introduction/Objective There exists contradictory evidence in the literature that ABO blood group may have some impact on risk of COVID-19 infection. Some argue that the blood group A may confer a higher susceptibility to infection and thus be overrepresented in those who test positive for COVID-19, though other studies in the literature do not support this. Therefore, we present a regional Veterans Administration Medical Center’s (VAMC) experience early in the pandemic to provide a reference on blood group and risk of testing positive early in the pandemic for a veteran population. Methods/Case Report A retrospective review of all positive SARS-CoV-2 reverse transcriptase polymerase chain reaction (RT-PCR) was performed for all RT-PCR tests collected at a regional VAMC from March 17th, 2020 to May 20th, 2020 was performed to collect ABO blood group information. Patients with no known ABO blood group were excluded. Results (if a Case Study enter NA) There were 81 patients who tested positive for SARS-CoV-2 with a known ABO blood group during the study period. This group had an age range of 45 to 99, consisted of 80 males and 1 female, and was racially proportioned at 57 African Americans (70.3%), 2 Asian Americans (2.5%), 1 Hispanic American (1.2%), 20 Caucasian Americans (24.7%), and one of unknown race (1.2%). The blood group distribution among these 81 patients was as follows: 39 were O+ (48.1%), 3 were O- (3.7%), 15 were B+ (18.5%), 3 were AB+ (3.7%), 18 A+ (22.2%), and 3 were A- (3.7%). Conclusion Comparison with the known distribution of ABO groups in the general population reveals that the proportion of blood groups of those testing positive are similar. This provides support to the proposition that the ABO type may not predispose significantly to COVID-19 infection.

The analysis of shotgun metagenomic data provides valuable insights into microbial communities, while allowing resolution at individual genome level. In absence of complete reference genomes, this requires the reconstruction of metagenome assembled genomes (MAGs) from sequencing reads. We present the nf-core/mag pipeline for metagenome assembly, binning and taxonomic classification. It can optionally combine short and long reads to increase assembly continuity and utilize sample-wise group-information for co-assembly and genome binning. The pipeline is easy to install - all dependencies are provided within containers -, portable and reproducible. It is written in Nextflow and developed as part of the nf-core initiative for best-practice pipeline development. All code is hosted on GitHub under the nf-core organization https://github.com/nf-core/mag and released under the MIT license.

ZnS materials have been widely used in fluorescence biosensors to characterize different types of stem cells due to their excellent fluorescence effect. In this study, ZnS was prepared by vulcanizing nano-Zn particles synthesized using a DC arc plasma. The composition and structure of the ZnS materials were studied by X-ray diffraction (XRD), and their functional group information and optical properties were investigated by using IR spectrophotometry and UV-vis spectrophotometry. It has been found that the synthesized materials consist of Zn, cubic ZnS, and hexagonal ZnS according to the vulcanization parameters. Crystalline ZnS was gradually transformed from a cubic to a hexagonal structure, and the cycling properties first increase, then decrease with increasing sulfurization temperature. There is an optimal curing temperature giving the best cycling performance and specific capacity: the material sulfurized thereat mainly consists of cubic β-ZnS phase with a small quantity of Zn and hexagonal α-ZnS. The cubic phase ZnS has better conductivity than hexagonal ZnS, as evinced by electrochemical impedance spectroscopy (EIS). The ZnS (as prepared) shows board absorption, which can be used in fluorescence biosensors in cell imaging systems.