Miracle-dz/UniFAR-FTUs huggingface dataset:
Innovation and Creativity

Jacques Barzun in his article “The Paradoxes of Creativity. ”1 How can you make something from nothing since, as William Shakespeare’s King Lear says, “Nothing will come of nothing? ” Yet organizations and individuals are bent on being creative, on being more creative, on being an “innovation leader. ” Charles Handy subtitled his most recent book The New Alchemists, “how visionary people make something out of nothing. ”2 Richard Florida has become well known for explicating the “creative class” and what it likes and where it lives. Editors at Fast Company dubbed the December 2004 edition of the magazine its “creativity issue. ” What’s going on here? Making something out of nothing used to be the prerogative of the gods.
Optothermal grid activation of microflow with magnetic nanoparticle thermophoresis for microfluidics

We report focused light-induced activation of intense magnetic microconvection mediated by suspended magnetic nanoparticles in microscale two-dimensional optothermal grids. Fully anisotropic control of microflow and mass transport fluxes is achieved by engaging the magnetic field along one or the other preferred directions. The effect is based on the recently described thermal diffusion–magnetomechanical coupling in synthetic magnetic nanofluids. We expect that the new phenomenon can be applied as an efficient all-optical mixing strategy in integrated microfluidic devices. This article is part of the theme issue ‘Transport phenomena in complex systems (part 2)’.
Herbal Plants as Feed Additives in Broiler Chicken Diets

Abstract This study evaluated the effect of some herbal plants as natural feed additives on broiler production parameters, carcass parts, and breast meat quality. A total of 150 broiler chicks, Cobb 500 hybrid, were allocated to five treatments with 30 broilers /group, for 42 days. The experimental treatments were: Control (C); a diet containing 5% alfalfa meal (A); a diet containing 5% alfalfa meal and 1% basil (AB); a diet containing 5% alfalfa and 1% thyme (AT) and a diet containing 5% alfalfa and 1% sage (AS). Bodyweight was significantly (P<0.05) affected in AS group compared with C and A groups. Feed conversion ratio was significantly higher only in A and AB groups. Experimental groups had significantly greater (P<0.05) gizzard weight. The total polyphenols content and the antioxidant capacity were significantly (P<0.05) higher in AB, AT, and AS groups versus the C group. The cholesterol concentration was significantly (P<0.05) decreased in AB, AT, and AS meat samples compared with C and A samples. Instrumental colour measurements indicated significant alterations (P<0.05) among the groups regarding the values of L*, a*, b*, H°, C*, and ΔE*. The texture analysis showed that hardness, gumminess, and chewiness were significantly (P<0.05) higher in AB, AT, and AS groups than in C and A groups.
Vibrotactile Data Physicalization: Exploratory Insights for Haptization of Low-resolution Images

We present a visuo-haptic interaction approach that augments the visual experience of graphic data visualization with meaningful vibrotactile information. The user of our interface explores charts and graphs on a conventional video monitor or other displays, e.g. projection, or printed paper. When a user touches the data visualization, an array of vibrotactile actuators placed at the joints on the back of their hand modulates frequency and amplitude to build signals that convey information, in a process of dynamic physicalization. We investigated the haptic perception of the method based on the change in the intensity of the simultaneous vibration and the distance between two variables being analyzed. For this, we assess a prototype of our interface in a user study (N=25) to understand how the stimulus is perceived and understood. Results show that different vibration intensities are recognized regardless of the spatial distance between them. The statistically significant difference in precision and response time between two values being haptically displayed shows it is possible to discriminate pieces of data with a faster response time as the percentage difference in intensity between them increases. Additionally, a heat map of the user interaction with the data represented by a square area shows that the user seeks to feel a single rendered data value on all actuators in the hand at once, rather than having two values rendered simultaneously on different fingers of the same hand.
Using Relational Inference Engine to Answer Questions

In this paper, we describe a fast chatbot learning methodology so that it can dynamically and automatically learn new information to deal more appropriately with new discourse domains. By our approach, the robot, named r AVA, creates internal structures of knowledge while reading more documents, which allow the chatbot to logical reasoning about the world’s facts. We experimented with our approach by reading additional documents, extracting family relationship information from the named entity recognized from the free-text contents. After reading these documents, r AVA can respond correctly to many questions presented by the users, providing them, by this way, with quick and personalized feedback evaluations.
Pre-6G Graduate Education of Communications Engineering

As sixth-generation (6G) communications have been widely discussed in the past 2 years, it is now the right time to investigate the potential impacts of 6G communications on the current graduate education system of communications engineering. In this article, we articulate a set of existing problems with the graduation education of communications engineering and analyze the trends and challenges of pre-6G graduate education of communications engineering. By this article, we not only aim to diagnose the existing problems with corresponding trends and challenges but also to call for proactive measures coping with them. Besides, we would also like to use this article to encourage more and more brave undergraduates to participate in communications engineering, a fast-changing and far-reaching discipline.
Divergent Abiotic Stressors Drive Grassland Community Assembly of Tibet and Mongolia Plateau

Multiple ecological processes simultaneously govern community assembly, but it remains unclear how abiotic stressors regulate the relative importance of these processes among different biogeographic regions. Therefore, we conducted a comprehensive study on the responses of community assembly to varying environmental gradients, using the mean, variance, skewness, and kurtosis of plant height (height), specific leaf area (SLA) and leaf dry matter content (LDMC) distributions on the Tibetan Plateau (TP) and the Mongolian Plateau (MP). Our results showed that the prevalence of trait convergence across all grasslands in both TP and MP seem to be the result of abiotic filtering or weaker competitive exclusion etc. These trait-convergence assembly processes decrease the functional dispersion but increase the evenness of the trait frequency distribution. The mean, variance, skewness, and kurtosis responses of grassland communities to abiotic stress varied between the TP and MP. On average, plant trait distribution was mainly driven by temperature on the TP, and low-temperature stress altered the community assembly rules. In contrast, water availability shaped plant trait frequency distributions on the MP, and drought stress mediated the balance between different assembly processes. Our results provide empirical evidence that divergent abiotic stressors regulate the grassland community assembly on the TP and MP. Together, our study speculates that different aspects of future climate change, such as climate warming and changing precipitation patterns, on community assembly are dependent on regional climatic regimes.
Inhibitory Control in Children 4–10 Years of Age: Evidence From Functional Near-Infrared Spectroscopy Task-Based Observations

Executive function (EF) is essential to child development, with associated skills beginning to emerge in the first few years of life and continuing to develop into adolescence and adulthood. The prefrontal cortex (PFC), which follows a neurodevelopmental timeline similar to EF, plays an important role in the development of EF. However, limited research has examined prefrontal function in young children due to limitations of currently available neuroimaging techniques such as functional resonance magnetic imaging (fMRI). The current study developed and applied a multimodal Go/NoGo task to examine the EF component of inhibitory control in children 4–10 years of age. Cortical activity was measured using a non-invasive and child-friendly neuroimaging technique – functional near-infrared spectroscopy (fNIRS). Children’s response accuracy and reaction times were captured during the fNIRS session and compared with responses obtained using the standardized assessments from NIH Toolbox cognition battery. Results showed significant correlations between the behavioral measures during the fNIRS session and the standardized EF assessments, in line with our expectations. Results from fNIRS measures demonstrated a significant, age-independent effect of inhibitory control (IC) in the right PFC (rPFC), and an age-dependent effect in the left orbitofrontal cortex (lOFC), consistent with results in previous studies using fNIRS and fMRI. Thus, the new task designed for fNIRS was suitable for examining IC in young children, and results showed that fNIRS measures can reveal prefrontal IC function.
Combining a Pharmacological Network Model with a Bayesian Signal Detection Algorithm to Improve the Detection of Adverse Drug Events

Introduction: Improving adverse drug event (ADE) detection is important for post-marketing drug safety surveillance. Existing statistical approaches can be further optimized owing to their high efficiency and low cost. Objective: The objective of this study was to evaluate the proposed approach for use in pharmacovigilance, the early detection of potential ADEs, and the improvement of drug safety. Methods: We developed a novel integrated approach, the Bayesian signal detection algorithm, based on the pharmacological network model (ICPNM) using the FDA Adverse Event Reporting System (FAERS) data published from 2004 to 2009 and from 2014 to 2019Q2, PubChem, and DrugBank database. First, we used a pharmacological network model to generate the probabilities for drug-ADE associations, which comprised the proper prior information component (IC). We then defined the probability of the propensity score adjustment based on a logistic regression model to control for the confounding bias. Finally, we chose the Side Effect Resource (SIDER) and the Observational Medical Outcomes Partnership (OMOP) data to evaluate the detection performance and robustness of the ICPNM compared with the statistical approaches [disproportionality analysis (DPA)] by using the area under the receiver operator characteristics curve (AUC) and Youden’s index. Results: Of the statistical approaches implemented, the ICPNM showed the best performance (AUC, 0.8291; Youden’s index, 0.5836). Meanwhile, the AUCs of the IC, EBGM, ROR, and PRR were 0.7343, 0.7231, 0.6828, and 0.6721, respectively. Conclusion: The proposed ICPNM combined the strengths of the pharmacological network model and the Bayesian signal detection algorithm and performed better in detecting true drug-ADE associations. It also detected newer ADE signals than a DPA and may be complementary to the existing statistical approaches.
The Inhibition Effect of Linezolid With Reyanning Mixture on MRSA and its Biofilm is More Significant than That of Linezolid Alone

Methicillin-resistant Staphylococcus aureus (MRSA) is a superbacterium, and when it forms biofilms, it is difficult to treat even with the first-line of antibiotic linezolid (LNZ). Reyanning mixture (RYN), a compound-based Chinese medicine formula, has been found to have inhibitory effects on biofilms. This study aims to explore the synergistic inhibitory effect and corresponding mechanisms of their (LNZ&RYN) combination on the planktonic as well as biofilm cells of MRSA. Broth microdilution and chessboard methods were employed for the determination of minimum inhibitory concentrations (MICs) and synergistic concentration of LNZ&RYN, respectively. The effect of the combined medication on biofilm and mature biofilm of MRSA were observed by biofilm morphology and permeability experiments, respectively. To unveil the molecular mechanism of action of the synergistic combination of LNZ and RYN, RT-PCR based biofilm-related gene expression analysis and ultra-high pressure liquid chromatography-time-of-flight mass spectrometry based endogenous metabonomic analysis were deployed. The results indicated that 1/16RYN as the best combined dose reduced LNZ (4 μg/ml) to 2 μg/ml. The combined treatment inhibited living MRSA before and after biofilm formation, removed the residual structure of dead bacteria in MRSA biofilms and affected the shape and size of bacteria, resulting in the improvement of biofilm permeability. The mechanism was that biofilm-related genes such as agrC, atlA, and sarA, as well as amino acid uptake associated with the metabolism of 3-dehydrocarnitine, kynurenine, L-leucine, L-lysine and sebacic acid were inhibited. This study provides evidence for the treatment of MRSA and its biofilms with LNZ combined with RYN.
The Gut Microbiome in Myalgic Encephalomyelitis (ME)/Chronic Fatigue Syndrome (CFS)

Myalgic encephalomyelitis (ME) or Chronic Fatigue Syndrome (CFS) is a neglected, debilitating multi-systemic disease without diagnostic marker or therapy. Despite evidence for neurological, immunological, infectious, muscular and endocrine pathophysiological abnormalities, the etiology and a clear pathophysiology remains unclear. The gut microbiome gained much attention in the last decade with manifold implications in health and disease. Here we review the current state of knowledge on the interplay between ME/CFS and the microbiome, to identify potential diagnostic or interventional approaches, and propose areas where further research is needed. We iteratively selected and elaborated on key theories about a correlation between microbiome state and ME/CFS pathology, developing further hypotheses. Based on the literature we hypothesize that antibiotic use throughout life favours an intestinal microbiota composition which might be a risk factor for ME/CFS. Main proposed pathomechanisms include gut dysbiosis, altered gut-brain axis activity, increased gut permeability with concomitant bacterial translocation and reduced levels of short-chain-fatty acids, D-lactic acidosis, an abnormal tryptophan metabolism and low activity of the kynurenine pathway. We review options for microbiome manipulation in ME/CFS patients including probiotic and dietary interventions as well as fecal microbiota transplantations. Beyond increasing gut permeability and bacterial translocation, specific dysbiosis may modify fermentation products, affecting peripheral mitochondria. Considering the gut-brain axis we strongly suspect that the microbiome may contribute to neurocognitive impairments of ME/CFS patients. Further larger studies are needed, above all to clarify whether D-lactic acidosis and early-life antibiotic use may be part of ME/CFS etiology and what role changes in the tryptophan metabolism might play. An association between the gut microbiome and the disease ME/CFS is plausible. As causality remains unclear, we recommend longitudinal studies. Activity levels, bedridden hours and disease progression should be compared to antibiotic exposure, drug intakes and alterations in the composition of the microbiota. The therapeutic potential of fecal microbiota transfer and of targeted dietary interventions should be systematically evaluated.
Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene–Gene and Gene–Diet Interactions

Obesity is associated with many chronic diseases that impair healthy aging and is governed by genetic, epigenetic, and environmental factors and their complex interactions. This study aimed to develop a model that predicts an individual’s risk of obesity by better characterizing these complex relations and interactions focusing on dietary factors. For this purpose, we conducted a combined genome-wide and epigenome-wide scan for body mass index (BMI) and up to three-way interactions among 402,793 single nucleotide polymorphisms (SNPs), 415,202 DNA methylation sites (DMSs), and 397 dietary and lifestyle factors using the generalized multifactor dimensionality reduction (GMDR) method. The training set consisted of 1,573 participants in exam 8 of the Framingham Offspring Study (FOS) cohort. After identifying genetic, epigenetic, and dietary factors that passed statistical significance, we applied machine learning (ML) algorithms to predict participants’ obesity status in the test set, taken as a subset of independent samples (n = 394) from the same cohort. The quality and accuracy of prediction models were evaluated using the area under the receiver operating characteristic curve (ROC-AUC). GMDR identified 213 SNPs, 530 DMSs, and 49 dietary and lifestyle factors as significant predictors of obesity. Comparing several ML algorithms, we found that the stochastic gradient boosting model provided the best prediction accuracy for obesity with an overall accuracy of 70%, with ROC-AUC of 0.72 in test set samples. Top predictors of the best-fit model were 21 SNPs, 230 DMSs in genes such as CPT1A, ABCG1, SLC7A11, RNF145, and SREBF1, and 26 dietary factors, including processed meat, diet soda, French fries, high-fat dairy, artificial sweeteners, alcohol intake, and specific nutrients and food components, such as calcium and flavonols. In conclusion, we developed an integrated approach with ML to predict obesity using omics and dietary data. This extends our knowledge of the drivers of obesity, which can inform precision nutrition strategies for the prevention and treatment of obesity. Clinical Trial Registration: [www.ClinicalTrials. gov], the Framingham Heart Study (FHS), [NCT00005121].
Neurophysiology of the Developing Cerebral Cortex: What We Have Learned and What We Need to Know

This review article aims to give a brief summary on the novel technologies, the challenges, our current understanding, and the open questions in the field of the neurophysiology of the developing cerebral cortex in rodents. In the past, in vitro electrophysiological and calcium imaging studies on single neurons provided important insights into the function of cellular and subcellular mechanism during early postnatal development. In the past decade, neuronal activity in large cortical networks was recorded in pre- and neonatal rodents in vivo by the use of novel high-density multi-electrode arrays and genetically encoded calcium indicators. These studies demonstrated a surprisingly rich repertoire of spontaneous cortical and subcortical activity patterns, which are currently not completely understood in their functional roles in early development and their impact on cortical maturation. Technological progress in targeted genetic manipulations, optogenetics, and chemogenetics now allow the experimental manipulation of specific neuronal cell types to elucidate the function of early (transient) cortical circuits and their role in the generation of spontaneous and sensory evoked cortical activity patterns. Large-scale interactions between different cortical areas and subcortical regions, characterization of developmental shifts from synchronized to desynchronized activity patterns, identification of transient circuits and hub neurons, role of electrical activity in the control of glial cell differentiation and function are future key tasks to gain further insights into the neurophysiology of the developing cerebral cortex.
Agricultural Advisory Diagnostics Using a Data-Based Approach: Test Case in an Intensively Managed Rural Landscape in the Ganga River Basin, India

technology adoption through agricultural extension may be a consequence of providing generic information without sufficient adaptation to local conditions. Data-rich paradigms may be disruptive to extension services and can potentially change farmer-advisor interactions. This study fills a gap in pre-existing, generic advisory programs by suggesting an approach to “diagnose” farm-specific agricultural issues quantitatively first in order to facilitate advisors in developing farm-centric advisories. A user-friendly Farm Agricultural Diagnostics (FAD) tool is developed in Microsoft Excel VBA that uses farmer surveys and soil testing to quantify current agricultural performance, classify farms into different performance categories relative to a localized performance target, and visualize farm performance within a user-friendly interface. The advisory diagnostics approach is tested in Kanpur, representative of an intensively managed rural landscape in the Ganga river basin in India. The developed open-source tool is made available online to generate data-based agricultural advisories. During the field testing in Kanpur, the tool identifies 24% farms as nutrient-limited, 34% farms as water-limited, 27% farms with nutrient and water co-limitations, and the remaining farms as satisfactory compared to the localized performance target. It is recommended to design advisories in terms of water and nutrient recommendations which can fulfill the farm needs identified by the tool. The tool will add data-based value to pre-existing demand based advisory services in agricultural extension programs. The primary users of the tools are academic, governmental and non-governmental agencies working in the agricultural sector, whose rigorous scientific research, soil testing capacity, and direct stakeholder engagement, respectively, can be harnessed to generate more data-based and customized advisories, potentially improving farmer uptake of agricultural advisories.
MEF2A Is the Trigger of Resveratrol Exerting Protection on Vascular Endothelial Cell

Both resveratrol and myocyte enhancer factor 2A (MEF2A) may protect vascular endothelial cell (VEC) through activating the expression of SIRT1. However, the relationship between resveratrol and MEF2A is unclear. We aimed to investigate the deeper mechanism of resveratrol in protecting vascular endothelial cells and whether MEF2A plays a key role in the protective function of resveratrol. Human umbilical vein endothelial cell (HUVEC) was used for in vitro study, and small interfere RNA was used for silencing MEF2A. Silencing MEF2A in the vascular endothelium (VE) of ApoE−/− mice was performed by tail injection with adeno associated virus expressing si-mef2a-shRNA. The results showed that treatment of HUVEC with resveratrol significantly up-regulated MEF2A, and prevented H2O2-induced but not siRNA-induced down-regulation of MEF2A. Under various experimental conditions, the expression of SIRT1 changed with the level of MEF2A. Resveratrol could rescue from cell apoptosis, reduction of cell proliferation and viability induced by H2O2, but could not prevent against that caused by silencing MEF2A with siRNA. Silencing MEF2A in VE of apoE−/− mice decreased the expression of SIRT1, increased the plasma LDL-c, and abrogated the function of resveratrol on reducing triglyceride. Impaired integrity of VE and aggravated atherosclerotic lesion were observed in MEF2A silenced mice through immunofluorescence and oil red O staining, respectively. In conclusion, resveratrol enhances MEF2A expression, and the upregulation of MEF2A is required for the endothelial protective benefits of resveratrol in vitro via activating SIRT1. Our work has also explored the in vivo relevance of this signaling pathway in experimental models of atherosclerosis and lipid dysregulation, setting the stage for more comprehensive phenotyping in vivo and further defining the molecular mechanisms.