Miracle-dz/UniFAR-FTUs huggingface dataset:
Tears for pears: Influence of children’s neophobia on categorization performance and strategy in the food domain

Preschoolers’ neophobic dispositions mainly target fruits and vegetables. They received a great deal of attention in the past decades as these dispositions represent the main psychological barrier to dietary variety. Recently, children’s food neophobia has been found to be negatively correlated with their categorization performance (i.e., the accuracy to discriminate between food categories). We investigated categorization strategies among neophobic children, tendencies to favor one type of error over the other (misses over false alarms), in order to compensate for their poor categorization performance. To capture children’s categorization strategies, we used the Signal Detection Theory framework. A first experiment assessed 120 3-to-6-years old children’ sensitivity to discriminate between foods and nonfoods as well as their decision criterion (i.e., response strategy). In a second experiment, we manipulated the influence of food processing. The hypothesis was that food processing acts as a sign of human interventions that decreases uncertainty about edibility and thus promotes feelings of safety in the food domain. 137 children were tested on a food versus nonfood categorization task contrasting whole and sliced stimuli. In both experiments, increased levels of food neophobia were significantly associated with poorer categorization sensitivity and with a more conservative decision criterion (i.e., favoring “it is inedible” errors). Additionally, results from Experiment 2 revealed that food processing did not influence neophobic children, whereas their neophilic counterparts adopted a more liberal decision criterion for sliced stimuli than for whole stimuli. These findings are the first demonstration of a relationship between a decision criterion and food neophobia in young children. These results have strong implications for theories of food neophobia and laid the groundwork for designing novel types of food education interventions.
Postbiotics: From emerging concept to application

The microbiome innovation has resulted in an umbrella term, postbiotics, which refers to non-viable microbial cells, metabolic byproducts and their microbial components released after lysis. Postbiotics, modulate immune response, gene expression, inhibit pathogen binding, maintain intestinal barriers, help in controlling carcinogenesis and pathogen infections. Postbiotics have antimicrobial, antioxidant, and immunomodulatory properties with favorable physiological, immunological, neuro-hormonal, regulatory and metabolic reactions. Consumption of postbiotics relieves symptoms of various diseases and viral infections such as SARS-CoV-2. Postbiotics can act as alternatives for pre-probiotic specially in immunosuppressed patients, children and premature neonates. Postbiotics are used to preserve and enhance nutritional properties of food, elimination of biofilms and skin conditioning in cosmetics. Postbiotics have numerous advantages over live bacteria with no risk of bacterial translocation from the gut to blood, acquisition of antibiotic resistance genes. The process of extraction, standardization, transport, and storage of postbiotic is more natural. Bioengineering techniques such as fermentation technology, high pressure etc., may be used for the synthesis of different postbiotics. Safety assessment and quality assurance of postbiotic is important as they may induce stomach discomfort, sepsis and/or toxic shock. Postbiotics are still in their infancy compared to pre- and pro- biotics but future research in this field may contribute to improved physiological functions and host health. The current review comprehensively summarizes new frontiers of research in postbiotics.
Variants in the zinc transporter-3 encoding gene (SLC30A3) in schizophrenia and bipolar disorder: Effects on brain glutamate–A pilot study

Zinc transporter 3 (ZnT3) has been implicated in the aetiopathology of schizophrenia. In this pilot study, we tested the hypothesis that the presence of a minor allele of two variants in the gene encoding ZnT3 (SLC30A3) affects brain glutamate and cognitive activity in patients with schizophrenia and bipolar affective disorder. Fifteen patients with schizophrenia (SCZ), 15 with bipolar affective disorder type 2 (BD), and 14 healthy volunteers (HV) were genotyped for two SLC30A3 single nucleotide polymorphisms (rs11126936 and rs11126929). They also underwent structural and functional MRI (n-back) imaging as well as static (PRESS) and functional magnetic resonance spectroscopy (n-back) on a 3 Tesla MRI system. SCZ with at least one copy of the minor allele showed reductions in dorsal anterior cingulate cortex glutamate during the n-back task, whereas SCZ without the minor allele showed an increase in glutamate. BD with the minor allele had reduced glutamate in the anterior cingulate cortex (p < 0.05). There was no effect of SLC30A3 genotype on BOLD activation during n-back or on cortical brain volume. This study supports the further investigation of SLC30A3 and its role in glutamatergic neurotransmission and in the neuropathology of mental illness.
Multifaceted analyses reveal carbohydrate metabolism mainly affecting the quality of postharvest bamboo shoots

Bamboo shoot is one of nutritious vegetables in China. However, the edible quality of fresh bamboo shoots deteriorates easily after harvest. Here, morphological, physiological, transcriptomic and microRNA sequencing analyses were conducted to investigate the postharvest characteristics of moso bamboo (Phyllostachys edulis) shoots. Rapid decreases of soluble sugars, structural polysaccharides and hydrolyzed tannins, and increases of lignin and condensed tannins were observed in the postharvest bamboo shoots. Differentially expressed genes (DEGs) and miRNAs with opposite trends were mainly enriched in structural polysaccharide metabolism, starch and sucrose metabolism and glycolysis pathways, which were consistent with the changes of carbohydrates. A co-expression network of carbohydrate metabolism was constructed, which was verified by qPCR and yeast one-hybrid (Y1H) assay. Furthermore, the function of one hub glycosyltransferase gene was validated in Arabidopsis, which confirmed that it was involved in xylan biosynthesis. These results are of great significance for revealing the carbohydrate metabolism mechanisms of postharvest bamboo shoots and provide a potential candidate gene for molecular breeding related to xylan in the future.
SUFFICIENT CONDITIONS FOR MOMENT APPROXIMATIONS FOR A SAMPLE RATIO OR REGRESSION COEFFICIENT UNDER SIMPLE RANDOM SAMPLING

SUMMARY. This paper discusses sufficient conditions for standard Taylor-expansion based approximations of the bias and mean squared error of a sample ratio or sample regression coefficient in finite population random sampling. No superpopulation model is used. For a sample ratio, the sufficient conditions permit some population units to have denominator terms Xi equal to zero; and require the other population units to have Xi values above bounds that are decreasing in the sample size n. For the regression case, the conditions involve trade-offs between sample size and spacing of the population of Xi values. For both the ratio and regression cases, the proposed conditinos give relatively simple forms to the intuitive idea that standard moment approximations are satisfactory except in certain extreme cases, where the definition of “extreme case” depends on the sample size. This paper discusses some conditions under which one can rigorously justify standard approximations for the moments of a sample ratio in the context of finite population sampling. The conditions emphasize trade-offs between sample size and the distribution of denominator items Xi; given a larger sample size, the Xi are allowed to be distributed more tightly around the origin. The ratio results are then extended to moment approximations for a sample regression coefficient. In an informal sense, one develops standard moment approximations by evaluating the leading terms of a Taylor expansion of the sample ratio. A rigorous application of this informal idea requires one to place small-order bounds on the moments of remainder terms of the Taylor expansion. One can obtain such bounds by assuming that all population units have denominator variables Xi
Body composition and cardiorespiratory fitness in overweight or obese people post COVID-19: A comparative study

The present study aimed to evaluate the body composition and cardiorespiratory fitness of overweight or obese people after COVID-19. 171 volunteers of both sexes (men, n = 93 and women, n = 78) between 19 and 65 years old were allocated into three groups according to the severity of their symptoms of COVID-19: non-hospitalized people/mild symptoms (n = 61), hospitalized (n = 58), and hospitalized in an intensive care unit-ICU (n = 52). Two laboratory visits were carried out 24 h apart. First, a medical consultation was carried out, with subsequent measurement of body weight and height (calculation of body mass index) and body composition assessment via electrical bioimpedance. After 24 h, a cardiorespiratory test was performed using the Bruce protocol, with a direct gas exchange analysis. Hospitalized individuals had significantly higher values for fat mass and body fat percentage than non-hospitalized individuals (p < 0.05). Significantly higher values were found for heart rate (HR) and peak oxygen consumption (VO2peak) for individuals who were not hospitalized when compared to those hospitalized in the ICU (p < 0.05). Significantly higher values for distance, ventilation, and the relationship between respiratory quotient were found for non-hospitalized individuals compared to hospitalized individuals and those in the ICU (p < 0.05). After the cardiorespiratory test, higher values for peripheral oxygen saturation (SpO2) were observed for non-hospitalized individuals than for all hospitalized individuals (p < 0.05). Diastolic blood pressure was significantly higher at the tenth and fifteenth minute post-Bruce test in hospitalized than in non-hospitalized participants (p < 0.05). Based on these results, proposals for cardiopulmonary rehabilitation are indispensable for hospitalized groups considering the responses of blood pressure. Monitoring HR, SpO2, and blood pressure are necessary during rehabilitation to avoid possible physical complications. Volume and intensity of exercise prescription should respect the physiologic adaptation. Given lower physical conditioning among all the groups, proposals for recovering from health conditions are urgent and indispensable for COVID-19 survivors.
Blockage of neuromuscular glutamate receptors impairs reinnervation following nerve crush in adult mice

Motor axons in peripheral nerves are capable of regeneration following injury. However, complete recovery of motor function is rare, particularly when reinnervation is delayed. We have previously found that glutamate receptors play a crucial role in the successful innervation of muscle during mouse development. In particular, blocking N-methyl-D-aspartate (NMDA) receptor activity delays the normal elimination of excess innervation of each neuromuscular junction. Here, we use behavioral, immunohistochemical, electrophysiological, and calcium imaging methods to test whether glutamate receptors play a similar role in the transition from polyneuronal to mono-innervation and in recovery of function following peripheral nerve injury in mature muscle.
Efficient shoot regeneration of medicinal plant Haplophyllum tuberculatum by direct and indirect organogenesis and genetic fidelity assessment using Inter Simple Sequence Repeats markers

In vitro plant cell and tissue cultures are potent tools to propagating germplasm resources in conserving and managing plant genetic resources. A reliable micropropagation protocol was developed for efficient callus proliferation and direct and indirect shoot regeneration of Meseika (Haplophyllum tuberculatum). With the applied sterilization procedure, immature, unopened H. tuberculatum seed pods can be identified as a potent explant with high viability and low contamination percentage. Multiple shoots were regenerated from leaf and stem explants through direct organogenesis on Murashige and Skoog’s (MS) + 3% sucrose medium amended with BAP. Indirect regeneration of several shoots was achieved on 1/2 MS + 1% sucrose media amended with 2 and 4 mg/l BAP. An efficient callus proliferation from both explants can be achieved by supplementing the MS media with NAA and BAP. All the cultures were incubated in a controlled growth chamber under 5/19 h light/dark photoperiod, temperature (25 ± 2°C), and 60% relative humidity (RH). 10 ISSR (Inter Simple Sequence Repeat) markers were screened to test the genetic fidelity of regenerated H. tuberculatum shoots. Callus development was observed after 15 days and shoot regeneration was occurred after 30 days after callus initiation. 10 ISSR primers produced a total of 39 clear, distinct amplicons. 75, 60, 40, and 16% polymorphism percentages were recorded by the ISSR primer 11, 7, 5, and 4, respectively. The developed micropropagation protocol is appropriate for rapid in-vitro multiplication of H. tuberculatum shoots and callus.
Innovation and authenticity: Constructing tourists’ subjective well-being in festival tourism

Although festival tourism is an excellent fertile ground for improving individual emotions, few studies have been conducted on the influencing factors and formation mechanisms of festival tourists’ subjective well-being. To address the current research gap, this paper draws on Arnold’ s theory of emotion to examine a comprehensive formation model of tourists’ subjective well-being. The findings from 581 samples indicate that event design innovation, cultural innovation and aesthetic innovation of festival tourism are positive stimulus factors of tourists’ subjective well-being. Both experience quality and perceived festival value mediate the effects of cultural innovation and aesthetic innovation on subjective well-being, yet have no mediating effect on the relationship between event design innovation and well-being. However, it can only be achieved when festival authenticity contributes to a positive moderating effect. This study provides new ideas for the collaborative advancement of innovative development and authentic inheritance in festival tourism destinations.
A comprehensive next generation sequencing tissue assay for Asian-prevalent cancers—Analytical validation and performance evaluation with clinical samples

Introduction: A well-validated diagnostic assay with curated biomarkers complements clinicopathological factors to facilitate early diagnosis and ensure timely treatment delivery. This study focuses on an Asian-centric cancer diagnostic assay designed and thoroughly validated against commercially available standard references and a cohort of over 200 clinical specimens spanning 12 diverse Asian-centric cancer types. Methods: The assay uses hybrid-capture probes capable of profiling DNA aberrations from 572 cancer-related genes and 91 RNA fusion partners. The panel can detect clinically-tractable biomarkers such as microsatellite instability (MSI) and tumor mutation burden (TMB). Results: Analytical evaluation demonstrated 100% specificity and 99.9% sensitivity within a ≥5% VAF limit of detection (LoD) for SNV/Indels. RNA-based fusion features an LoD of ≥5 copies per nanogram input when evaluated against commercial references. Excellent linearity and concordance were observed when benchmarking against orthogonal methods in identifying MSI status, TMB scores and RNA fusions. Actionable genetic alterations were identified in 65% of the clinical samples. Conclusion: These results demonstrate a molecular diagnostic assay that accurately detects genomic alterations and complex biomarkers. The data also supports an excellent performance of this assay for making critical diagnoses and well-informed therapeutic decisions in Asian prevalent cancers.
Coarse-to-fine evolutionary search for large-scale multi-objective optimization: An application to ratio error estimation of voltage transformers

Multi-objective optimization problems (MOPs) are commonly confronted in various fields, such as condition monitoring for renewable energy systems, and ratio error estimation of voltage transformers. With the increase in decision variables of MOPs, their exponentially growing search spaces are challenging for existing evolutionary algorithms. To handle this challenge, this paper suggests a coarse-to-fine large-scale evolutionary multi-objective search, called CF-LEMO. In the coarse search phase, CF-LEMO performs evolutionary search on both the original and transformed large-scale MOPs alternately, accelerating the population to approach the Pareto-optimal fronts. In addition, to alleviate the issue of diversity loss, we design a diversity preservation mechanism to preserve a well-distributed archive to support subsequent fine search. In the fine search stage, CF-LEMO conducts local search on the current population to mine high-quality solutions, which are used to update the population and archive. Then, based on the archive, the multi-objective optimization based on decomposition is employed to evolve all decision variables, so as to obtain a population with good convergence and diversity near the Pareto-optimal fronts. To assess the effectiveness of the proposed CF-LEMO, we compare its performance against four representative baseline algorithms on a benchmarks suite LSMOP1-LSMOP9 with 2 and 3 objectives. The empirical results confirm its super performance by significanlty outperforming all the four competitors on 12 out of 18 benchmarks. Moreover, the experiments demonstrate the superior performance of CF-LEMO in sovling multi-objective ratio error estimation problems with up to 6,000 decision variables.
MARIO: Modular and Extensible Architecture for Computing Visual Statistics in RoboCup SPL

This technical report describes a modular and extensible architecture for computing visual statistics in RoboCup SPL (MARIO), presented during the SPL Open Research Challenge at RoboCup 2022, held in Bangkok (Thailand). MARIO is an open-source, ready-to-use software application whose final goal is to contribute to the growth of the RoboCup SPL community. MARIO comes with a GUI that integrates multiple machine learning and computer vision based functions, including automatic camera calibration, background subtraction, homography computation, player + ball tracking and localization, NAO robot pose estimation and fall detection. MARIO has been ranked no. 1 in the Open Research Challenge.
Performance Impact of Channel Aging and Phase Noise on Intelligent Reflecting Surface

This letter aims to clarify the impact of channel aging and phase noise on the performance of intelligent reflecting surface-aided wireless systems. We first model mathematically the outdated channel state information (CSI) due to Doppler shifts and phase noise stemming from hardware impairment. Then, a closed-form expression of achievable spectral efficiency under noisy and aged CSI is theoretically derived. Some typical simulation results to numerically demonstrate the performance impact are illustrated.
Report of the Topical Group on Quantum Sensors for Snowmass 2021

Quantum Sensors offer great potential for providing enhanced sensitivity in high energy physics experiments. In this report we provide a summary of key quantum sensors technologies - interferometers, optomechanics, and clocks; spin dependent sensors; superconducting sensors; and quantum calorimeters - highlighting existing experiments along with areas for development. We also provide a set of key messages intended to further advance the state of quantum sensors used for high energy physics specific applications.
Improved Marginal Unbiased Score Expansion (MUSE) via Implicit Differentiation

We apply the technique of implicit differentiation to boost performance, reduce numerical error, and remove required user-tuning in the Marginal Unbiased Score Expansion (MUSE) algorithm for hierarchical Bayesian inference. We demonstrate these improvements on three representative inference problems: 1) an extended Neal's funnel 2) Bayesian neural networks, and 3) probabilistic principal component analysis. On our particular test cases, MUSE with implicit differentiation is faster than Hamiltonian Monte Carlo by factors of 155, 397, and 5, respectively, or factors of 65, 278, and 1 without implicit differentiation, and yields good approximate marginal posteriors. The Julia and Python MUSE packages have been updated to use implicit differentiation, and can solve problems defined by hand or with any of a number of popular probabilistic programming languages and automatic differentiation backends.
Understanding Exchange Rates Dynamics

With the emergence of the chaos theory and the method of surrogates data, nonlinear approaches employed in analysing time series typically suffer from high computational complexity and lack of straightforward explanation. Therefore, the need for methods capable of characterizing time series in terms of their linear, nonlinear, deterministic and stochastic nature are preferable. In this paper, we provide a signal modality analysis on a variety of exchange rates. The analysis is achieved by using the recently proposed "delay vector variance" (DVV) method, which examines local predictability of a signal in the phase space to detect the presence of determinism and nonlinearity in a time series. Optimal embedding parameters used in the DVV analysis are obtain via differential entropy based method using wavelet-based surrogates. A comprehensive analysis of the feasibility of this approach is provided. The empirical results show that the DVV method can be opted as an alternative way to understanding exchange rates dynamics.
Unifying Classification Schemes for Software Engineering Meta-Research

Background: Classifications in meta-research enable researchers to cope with an increasing body of scientific knowledge. They provide a framework for, e.g., distinguishing methods, reports, reproducibility, and evaluation in a knowledge field as well as a common terminology. Both eases sharing, understanding and evolution of knowledge. In software engineering (SE), there are several classifications that describe the nature of SE research. Regarding the consolidation of the large body of classified knowledge in SE research, a generally applicable classification scheme is crucial. Moreover, the commonalities and differences among different classification schemes have rarely been studied. Due to the fact that classifications are documented textual, it is hard to catalog, reuse, and compare them. To the best of our knowledge, there is no research work so far that addresses documentation and systematic investigation of classifications in SE meta-research. Objective: We aim to construct a unified, generally applicable classification scheme for SE meta-research by collecting and documenting existing classification schemes and unifying their classes and categories. Method: Our execution plan is divided into three phases: construction, validation, and evaluation phase. For the construction phase, we perform a literature review to identify, collect, and analyze a set of established SE research classifications. In the validation phase, we analyze individual categories and classes of included papers. We use quantitative metrics from literature to conduct and assess the unification process to build a generally applicable classification scheme for SE research. Lastly, we investigate the applicability of the unified scheme. Therefore, we perform a workshop session followed by user studies w.r. t. investigations about reliability, correctness, and ease of use.