Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceptual Fluency Affects Judgments of Learning Non-analytically and Analytically Through Beliefs About How Perceptual Fluency Affects Memory

Perceptual fluency is generally thought to affect judgments of learning (JOLs) non-analytically. However, some studies suggested that perceptual fluency may also affect JOLs analytically based on beliefs about the relationship between perceptual fluency and memory performance. The present study aimed to investigate how perceptual fluency affects JOLs. In Experiment 1, participants performed a continuous identification task and a JOLs task to determine whether perceptual fluency affects JOLs. In Experiment 2, we manipulated participants’ beliefs about how perceptual fluency affects memory to explore whether perceptual fluency affects JOLs through belief-based analysis. In Experiment 3, we explored whether participants who believed neither perceptual fluency nor font size affected memory performance still offered higher JOLs to large words than to small words, to explore whether perceptual fluency affects JOLs non-analytically. In Experiment 4, participants performed a continuous identification-JOLs task, and then they performed an observation task to measure their beliefs about fluency and memory. The results of the four experiments suggested that perceptual fluency affects JOLs both non-analytically and analytically based on beliefs about the relationship between perceptual fluency and memory performance.
Tuning Higher Order Structure in Colloidal Fluids

Colloidal particles self assemble into a wide range of structures under AC electric fields due to induced dipolar interactions [Yethiraj and Van Blaaderen Nature 421 513 (2003)]. Tuning the external field drives structural transitions in the system. Here we consider colloidal fluids comprised of such dipolar particles in experiment and simulation and investigate their 2-body and higher-order structure using a variety of probes. The higher--order structure we probe using three-body spatial correlation functions and a many--body approach based on minimum energy clusters of a related system. These clusters exhibit a cascade of geometric transitions upon increasing the strength of the dipolar interaction, which are echoed in the higher--order structure of the concentrated fluids we study here.
The Interrelationship Between Microbiota and Peptides During Ripening as a Driver for Parmigiano Reggiano Cheese Quality

Cheese microbiota contribute significantly to the final characteristics of cheeses due to the growth and interaction between cheese microorganisms during processing and ripening. For raw milk cheeses, such as Parmigiano Reggiano (PR), the microbiota derive from the raw milk itself, the dairy environment, and the starter. The process of cheese making and time of ripening shape this complex ecosystem through the selection of different species and biotypes that will drive the quality of the final product by performing functions of their metabolism such as proteolysis. The diversity in the final peptide and amino acid composition of the cheese is thus mostly linked to the diversity of this microbiota. The purpose of this study was to get more insight into the factors affecting PR cheese diversity and, more specifically, to evaluate whether the composition of the bacterial community of cheeses along with the specific peptide composition are more affected by the ripening times or by the cheese making process. To this end, the microbiota and the peptide fractions of 69 cheese samples (from curd to cheese ripened 24 months) were analyzed during 6 complete PR production cycles, which were performed in six different dairies located in the PR production area. The relation among microbial dynamics, peptide evolution, and ripening times were investigated in this unique and tightly controlled production and sampling set up. The study of microbial and peptide moieties in products from different dairies – from curd to at least 12 months, the earliest time from which the cheese can be sold, and up to a maximum of 24 months of ripening – highlighted the presence of differences between samples coming from different dairies, probably due to small differences in the cheese making process. Besides these differences, however, ripening time had by far the greatest impact on microbial dynamics and, consequently, on peptide composition.
Emotional, Behavioral, and Psychological Impact of the COVID-19 Pandemic

The emergence of SARS-CoV-2 in December 2019 prompted consternation in many parts of the world. Due to its fast dissemination, the World Health Organization declared a pandemic in March 2020. Aiming to contain the spread of the virus, leaders of many countries restrained social movement, targeting to flatten the curve of contamination with social distancing. This review aimed to analyze how human behavior has changed throughout this period. We also approached the key components of the emotional reaction to the pandemic, how internal and external factors, such as personality traits, gender, the media, the economy and the governmental response, influence the social perception of the pandemic and the psychological outcomes of the current scenario. Moreover, we explored in depth the groups at increased risk of suffering mental health burden secondary to these circumstances. These include the healthcare professionals, elderly individuals, children, college students, black subjects, latin and LGBTQ+ communities, economically disadvantaged groups, the homeless, prisoners, the rural population and psychiatric patients. We also discussed several measures that might minimize the emotional impact derived from this scenario. It is crucial that the health authorities, the government and the population articulate to assist the vulnerable groups and promote emotional and psychological support strategies. Moreover, it is fundamental that the population is provided with accurate information concerning the COVID-19 pandemic.
Student use of social media, academic performance, and creativity: the mediating role of intrinsic motivation

The purpose of this paper is to investigate the relationships between students’ use of social media, their academic performance and creativity in the digital era. ,This research used a survey strategy for collecting primary data required for this study from 334 students of undergraduate programs at Chinese universities who were sampled through a non-probability convenience approach. ,The findings of the study reveal that students’ use of social media is positively associated with students’ academic performance and creativity. In addition, intrinsic motivation was found to be a mediating reason in the relationships between students’ use of social media and students’ academic performance and creativity. ,This study explored an important role of intrinsic motivation as a mediator in the relationships between students’ use of social media and their positive outcomes. Studying the use of social media by students to their positive study outcomes is also practically important for students, educationalists and other policymakers.
Part-List Cues Hinder Familiarity but Not Recollection in Item Recognition: Behavioral and Event-Related Potential Evidence

Participants’ memory performance is normally poorer when a subset of previously learned items is provided as retrieval cues than none of the retrieval cues is provided. This phenomenon is called the part-list cuing effect, which has been discovered in numerous behavioral studies. However, there is currently no relevant behavioral or event-related potential (ERP) research to investigate whether the forgetting effect caused by part-list cues is more sensitive to recollection or to familiarity. By combining the part-list cuing paradigm with the Remember/Know procedure, we investigated this issue in the present ERP study. Behavioral data showed part-list cuing induced detrimental effect in two aspects: significantly lowered familiarity of the target items and decreased memory discrimination score (Pr score) for “Know” but not for “Remember” items in the part-list cue condition than in the no-part-list cue condition. ERP data revealed that the FN400 old/new effects, which are associated with familiarity, were absent when providing part-list cues, whereas the late positive complex (LPC) old/new effects, which are associated with recollection, were observed comparably in both part-list cue and no-part-list cue conditions. Converging behavioral and ERP results suggested that part-list cues hindered familiarity-based retrieval but not recollection-based retrieval of item recognition. Theoretical implications of the findings for the part-list cuing effect are discussed.
Metabolomics Pilot Study Identifies Desynchronization of 24-H Rhythms and Distinct Intra-patient Variability Patterns in Critical Illness: A Preliminary Report

Background: Synchronized circadian rhythms play a key role in coordinating physiologic health. Desynchronized circadian rhythms may predispose individuals to disease or be indicative of underlying disease. Intensive care unit (ICU) patients likely experience desynchronized circadian rhythms due to disruptive environmental conditions in the ICU and underlying pathophysiology. This observational pilot study was undertaken to determine if 24-h rhythms are altered in ICU patients relative to healthy controls by profiling 24-h rhythms in vital signs and plasma metabolites. Methods: We monitored daily rhythms in 5 healthy controls and 5 ICU patients for 24 h. Heart rate and blood pressure were measured every 30 min, temperature was measured every hour, and blood was sampled for mass spectrometry-based plasma metabolomics every 4 h. Bedside sound levels were measured every minute. Twenty-four hours rhythms were evaluated in vitals and putatively identified plasma metabolites individually and in each group using the cosinor method. Results: ICU patient rooms were significantly louder than healthy controls' rooms and average noise levels were above EPA recommendations. Healthy controls generally had significant 24-h rhythms individually and as a group. While a few ICU patients had significant 24-h rhythms in isolated variables, no significant rhythms were identified in ICU patients as a group, except in cortisol. This indicates a lack of coherence in phases and amplitudes among ICU patients. Finally, principal component analysis of metabolic profiles showed surprising patterns in plasma sample clustering. Each ICU patient's samples were clearly discernable in individual clusters, separate from a single cluster of healthy controls. Conclusions: In this pilot study, ICU patients' 24-h rhythms show significant desynchronization compared to healthy controls. Clustering of plasma metabolic profiles suggests that metabolomics could be used to track individual patients' clinical courses longitudinally. Our results show global disordering of metabolism and the circadian system in ICU patients which should be characterized further in order to determine implications for patient care.
Using Images of Eyes to Enhance Green Brand Purchase Intentions Through Green Brand Anthropomorphism Strategies: The Moderator Role of Facial Expression

This study uses an experimental comparison to analyze the effects of anthropomorphic strategies that use images with eyes (vs. those without eyes) on consumers’ willingness to buy green products, as well as the mechanism of action. The study finds that concerning the anthropomorphic features of a product, anthropomorphic strategies containing images of eyes lead to more positive purchase intention for green products than those without images of eyes. Simultaneously, these green purchase intentions are mediated through the variable of green trust regardless of whether the anthropomorphic strategies feature eyes. Beyond this, the discussed effects are moderated when anthropomorphic strategies featuring different facial expressions are used. The findings of this study provide useful suggestions for green brand marketing strategies and management practices that use anthropomorphism.
Atomic Force Microscopy (AFM) As a Surface Mapping Tool in Microorganisms Resistant Toward Antimicrobials: A Mini-Review

The worldwide emergence of antimicrobial resistance (AMR) in pathogenic microorganisms, including bacteria and viruses due to a plethora of reasons, such as genetic mutation and indiscriminate use of antimicrobials, is a major challenge faced by the healthcare sector today. One of the issues at hand is to effectively screen and isolate resistant strains from sensitive ones. Utilizing the distinct nanomechanical properties (e.g., elasticity, intracellular turgor pressure, and Young’s modulus) of microbes can be an intriguing way to achieve this; while atomic force microscopy (AFM), with or without modification of the tips, presents an effective way to investigate such biophysical properties of microbial surfaces or an entire microbial cell. Additionally, advanced AFM instruments, apart from being compatible with aqueous environments—as often is the case for biological samples—can measure the adhesive forces acting between AFM tips/cantilevers (conjugated to bacterium/virion, substrates, and molecules) and target cells/surfaces to develop informative force-distance curves. Moreover, such force spectroscopies provide an idea of the nature of intercellular interactions (e.g., receptor-ligand) or propensity of microbes to aggregate into densely packed layers, that is, the formation of biofilms—a property of resistant strains (e.g., Staphylococcus aureus, Pseudomonas aeruginosa). This mini-review will revisit the use of single-cell force spectroscopy (SCFS) and single-molecule force spectroscopy (SMFS) that are emerging as powerful additions to the arsenal of researchers in the struggle against resistant microbes, identify their strengths and weakness and, finally, prioritize some future directions for research.
Early Shift of Attention Is Not Regulated by Mind Wandering in Visual Search

Unique to humans is the ability to report subjective awareness of a broad repertoire of external and internal events. Even when asked to focus on external information, the human’s mind repeatedly wanders to task-unrelated thoughts, which limits reading comprehension or the ability to withhold automated manual responses. This led to the attentional decoupling account of mind wandering (MW). However, manual responses are not an ideal parameter to study attentional decoupling, given that during MW, the online adjustment of manual motor responses is impaired. Hence, whether early attentional mechanisms are indeed downregulated during MW or only motor responses being slowed is not clear. In contrast to manual motor responses, eye movements are considered a sensitive proxy of attentional shifts. Using a simple target detection task, we asked subjects to indicate whether a target was presented within a visual search display by pressing a button while we recorded eye movements and unpredictably asked the subjects to rate their actual level of MW. Generally, manual reaction times increased with MW, both in target absent and present trials. But importantly, even in trials with MW, subjects detected earlier a presented than an absent target. The decoupling account would predict more fixations of the target before pressing the button during MW. However, our results did not corroborate this assumption. Most importantly, subject’s time to direct gaze at the target was equally fast in trials with and without MW. Our results corroborate our hypothesis that during MW early, bottom–up driven attentional processes are not decoupled but selectively manual motor responses are slowed.
An Evaluation of The Utility of Simple Sequence Repeat loci (SSR) ,Expressed Sequence Tags (ESTs) and Expressed Sequence Tag Microsatellites (EST-SSR) as Molecular Markers in Cotton

The present study was undertaken with 21 SSR, 10 EST-SSR and 7 EST primer pairs for genetic diversity estimation in 14 cotton genotypes including 13 G. barbadense genotypes, i.e., G45, G70, G75, G76, G77, G80, G83, G85, G86, G87, G88, G89 and G90 and only one genotype (Hindi) belonging to G. hirsutum. Analysis of the 1 4 cotton geno types as revealed by 7 E ST and 31 SSR and EST- SSR primer pairs resulted in 119 alleles among which 79 were polymorphic (66.4%). The number of alleles p er pri mer pair ranged from 1 t o 6, except M7 which reveal ed eleven alleles, with an average of 3.1. While t he number of polymorphic a lleles ranged f rom 0 to 5 with a n a verage of 2.1 al leles per primer pair. The all ele size varied f rom 80 to 45 0 bp and t he genetic s imilarities among all genotypes ranged from 68.9 (between Hindi and G45) t o 95.5 (between G70 and G76) i n co mbined data. The classification of the cotton genotypes using the UPGMA grouped cotton genotypes into two clusters. These markers were successful in charact erizing ni ne o ut of the 14 tested cotton geno types by unique p ositive and /or n egative markers. P IC values d etected by S SR primer p airs ranged from 0.06 in M55 to 0.98 in M23, while it ranged from 0. 24 i n C7 to 0. 92 i n C9 in EST-SSR and i n EST, the PIC value ranged from 0.14 in EST-E3 to 0.67 in EST-E8.
Host Genetic Variants Potentially Associated With SARS-CoV-2: A Multi-Population Analysis

Background Clinical outcomes of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) showed enormous inter-individual and inter-population differences, possibly due to host genetics differences. Earlier studies identified single nucleotide polymorphisms (SNPs) associated with SARS-CoV-1 in Eastern Asian (EAS) populations. In this report, we aimed at exploring the frequency of a set of genetic polymorphisms that could affect SARS-CoV-2 susceptibility or severity, including those that were previously associated with SARS-CoV-1. Methods We extracted the list of SNPs that could potentially modulate SARS-CoV-2 from the genome wide association studies (GWAS) on SARS-CoV-1 and other viruses. We also collected the expression data of these SNPs from the expression quantitative trait loci (eQTLs) databases. Sequences from Qatar Genome Programme (QGP, n = 6,054) and 1000Genome project were used to calculate and compare allelic frequencies (AF). Results A total of 74 SNPs, located in 10 genes: ICAM3, IFN-γ, CCL2, CCL5, AHSG, MBL, Furin, TMPRSS2, IL4, and CD209 promoter, were identified. Analysis of Qatari genomes revealed significantly lower AF of risk variants linked to SARS-CoV-1 severity (CCL2, MBL, CCL5, AHSG, and IL4) compared to that of 1000Genome and/or the EAS population (up to 25-fold change). Conversely, SNPs in TMPRSS2, IFN-γ, ICAM3, and Furin were more common among Qataris (average 2-fold change). Inter-population analysis showed that the distribution of risk alleles among Europeans differs substantially from Africans and EASs. Remarkably, Africans seem to carry extremely lower frequencies of SARS-CoV-1 susceptibility alleles, reaching to 32-fold decrease compared to other populations. Conclusion Multiple genetic variants, which could potentially modulate SARS-CoV-2 infection, are significantly variable between populations, with the lowest frequency observed among Africans. Our results highlight the importance of exploring population genetics to understand and predict COVID-19 outcomes. Indeed, further studies are needed to validate these findings as well as to identify new genetic determinants linked to SARS-CoV-2.
Deep Neural Network Algorithm Feedback Model with Behavioral Intelligence and Forecast Accuracy

When attempting to apply a large-scale database that holds the behavioral intelligence training data of deep neural networks, the classification accuracy of the artificial intelligence algorithm needs to reflect the behavioral characteristics of the individual. When a change in behavior is recognized, that is, a feedback model based on a data connection model is applied, an analysis of time series data is performed by extracting feature vectors and interpolating data in a deep neural network to overcome the limitations of the existing statistical analysis. Using the results of the first feedback model as inputs to the deep neural network and, furthermore, as the input values of the second feedback model, and interpolating the behavioral intelligence data, that is, context awareness and lifelog data, including physical activities, involves applying the most appropriate conditions. The results of this study show that this method effectively improves the accuracy of the artificial intelligence results. In this paper, through an experiment, after extracting the feature vector of a deep neural network and restoring the missing value, the classification accuracy was verified to improve by about 20% on average. At the same time, by adding behavioral intelligence data to the time series data, a new data connection model, the Deep Neural Network Feedback Model, was proposed, and it was verified that the classification accuracy can be improved by about 8 to 9% on average. Based on the hypothesis, the F (X′) = X model was applied to thoroughly classify the training data set and test data set to present a symmetrical balance between the data connection model and the context-aware data. In addition, behavioral activity data were extrapolated in terms of context-aware and forecasting perspectives to prove the results of the experiment.
Low-cost Security for Next-generation IoT Networks

In recent years, the ubiquitous nature of Internet-of-Things (IoT) applications as well as the pervasive character of next-generation communication protocols, such as the 5G technology, have become widely evident. In this work, we identify the need for low-cost security in current and next-generation IoT networks and address this demand through the implementation, testing, and validation of an intrinsic low-cost and low-overhead hardware-based security primitive within an inherent network component. In particular, an intrinsic Physical Unclonable Function (PUF) is implemented in the peripheral network module of a tri-band commercial off-the-shelf router. Subsequently, we demonstrate the robustness of this PUF to ambient temperature variations and to limited natural aging, and examine in detail its potential for securing the next generation of IoT networks and other applications. Finally, the security of the proposed PUF-based schemes is briefly assessed and discussed.