Miracle-dz/UniFAR-FTUs huggingface dataset:
New statistical methodologies for improved analysis of genomic and omic data

We develop statistical tools for analyzing different types of phenotypic data in genomewide settings. When the phenotype of interest is a binary case-control status, most genome-wide association studies (GWASs) use randomly selected samples from the population (hereafter bases) as the control set. This approach is successful when the trait of interest is very rare; otherwise, a loss in the statistical power to detect diseaseassociated variants is expected. To address this, we propose a joint analysis of the three types of samples; cases, bases and controls. This is done by modeling the bases as a mixture of multinomial logistic functions of cases and controls, according to disease prevalence. In a typical GWAS, where thousands of single-nucleotide polymorphisms (SNPs) are available for testing, score-based test statistics are ideal in this case. Other tests of associations such as Wald’s and likelihood ratio tests are known to be asymptotically equivalent to the score test, however their performance under small sample sizes can vary significantly. In order to allow the test comparison to be performed under the proposed case-base-control (CBC) design, we provide an estimation procedure using the maximum likelihood (ML) method along with the expectation-maximization (EM) algorithm. Simulations show that combining the three samples can increase the power to detect disease-associated variants, though a very large base sample set can compensate for lack of controls. In the second part of the thesis, we consider a joint analysis of both genome-wide SNPs as well as multiple phenotypes, with a focus on the challenges they present in the estimation of SNP heritability. The current standard for performing this task is fitting a variance component model, despite its tendency to produce boundary estimates when small sample sizes are used. We propose a Bayesian covariance component model 2 (BCCM) that takes into account genetic correlation among phenotypes and genetic correlation among individuals. The use of Bayesian methods allows us to circumvent some issues related to small sample sizes, mainly overfitting and boundary estimates. Using gene expression pathways, we demonstrate a significant improvement in SNP heritability estimates over univariate and ML-based methods, thus explaining why recent progress in eQTL identification has been limited. I published this work as an article in the European Journal of Human genetics. In the third part of the thesis, we study the prospects of using the proposed BCCM for phenotype prediction. Results from real data show consistency in accuracy between ML based methods and the proposed Bayesian method, when effect sizes are estimated using their posterior mode. It is also noted that an initial imputation step relatively increases the predictive accuracy.
The Economics of Religious Communities

The religious club model is central to the economics of religion. To extend the scope of its application, we develop the first club model to combine (i) network externalities, (ii) discrimination against members, and (iii) competition among religious leaders. This leads to new insights into religious communities. Social integration by a religious minority depends on economic development, religious commitment, discrimination, and religious competition. These factors interact with the internal organization of the community to produce distinctive patterns of behavior. A rising share of community members with high religious commitment fractures the community, producing either assimilation by moderates or schism. Once a critical level is reached, social integration declines rapidly with religious commitment. Blanket discrimination against all community members makes the religious community stricter and more cohesive. Stigmatizing active religious participation promotes social integration on the whole, but can create an extreme isolationist sect. Religious competition reduces religious participation and boosts social integration. The results provide guidance for empirical work on religious discrimination.
Quantifying tensile forces at cell–cell junctions with a DNA-based fluorescent probe†

Cells are physically contacting with each other. Direct and precise quantification of forces at cell–cell junctions is still challenging. Herein, we have developed a DNA-based ratiometric fluorescent probe, termed DNAMeter, to quantify intercellular tensile forces. These lipid-modified DNAMeters can spontaneously anchor onto live cell membranes. The DNAMeter consists of two self-assembled DNA hairpins of different force tolerance. Once the intercellular tension exceeds the force tolerance to unfold a DNA hairpin, a specific fluorescence signal will be activated, which enables the real-time imaging and quantification of tensile forces. Using E-cadherin-modified DNAMeter as an example, we have demonstrated an approach to quantify, at the molecular level, the magnitude and distribution of E-cadherin tension among epithelial cells. Compatible with readily accessible fluorescence microscopes, these easy-to-use DNA tension probes can be broadly used to quantify mechanotransduction in collective cell behaviors.
Amplicon sequencing of the 16S-ITS-23S rRNA operon with long-read technology for improved phylogenetic classification of uncultured prokaryotes

Amplicon sequencing of the 16S rRNA gene is the predominant method to quantify microbial compositions of environmental samples and to discover previously unknown lineages. Its unique structure of interspersed conserved and variable regions is an excellent target for PCR and allows for classification of reads at all taxonomic levels. However, the relatively few phylogenetically informative sites prevent confident phylogenetic placements of novel lineages that are deep branching relative to reference taxa. This problem is exacerbated when only short 16S rRNA gene fragments are sequenced. To resolve their placement, it is common practice to gather more informative sites by combining multiple conserved genes into concatenated datasets. This however requires genomic data which may be obtained through relatively expensive metagenome sequencing and computationally demanding analyses. Here we develop a protocol that amplifies a large part of 16S and 23S rRNA genes within the rRNA operon, including the ITS region, and sequences the amplicons with PacBio long-read technology. We tested our method with a synthetic mock community and developed a read curation pipeline that reduces the overall error rate to 0.18%. Applying our method on four diverse environmental samples, we were able to capture near full-length rRNA operon amplicons from a large diversity of prokaryotes. Phylogenetic trees constructed with these sequences showed an increase in statistical support compared to trees inferred with shorter, Illumina-like sequences using only the 16S rRNA gene (250 bp). Our method is a cost-effective solution to generate high quality, near full-length 16S and 23S rRNA gene sequences from environmental prokaryotes.
Enigma homolog 1 scaffolds protein kinase D1 to regulate the activity of the cardiac L-type voltage-gated calcium channel.

AIMS In cardiomyocytes, protein kinase D1 (PKD1) plays a central role in the response to stress signals. From a yeast two-hybrid assay, we have identified Enigma Homolog 1 (ENH1) as a new binding partner of PKD1. Since in neurons, ENH1, associated with protein kinase Cepsilon, was shown to modulate the activity of N-type calcium channels, and the pore-forming subunit of the cardiac L-type voltage-gated calcium channel, alpha1C, possesses a potential phosphorylation site for PKD1, we studied here a possible role of ENH1 and PKD1 in the regulation of the cardiac L-type voltage-gated calcium channel. METHODS AND RESULTS PKD1-interacting proteins were searched by yeast two-hybrid screening. In vivo protein interactions in cardiomyocytes isolated from heart ventricles of newborn rats were tested by co-immunoprecipitation. Small interfering RNA and a dominant negative mutant of PKD1 were delivered into cardiomyocytes by use of an adenovirus. Calcium currents were measured by the patch-clamp technique. Both ENH1 and PKD1 interact with alpha1C in cardiomyocytes. This interaction is increased upon stimulation. Silencing of ENH1 prevented the binding of PKD1 to alpha1C. Moreover, a dominant negative mutant of PKD1 or the silencing of ENH1 inhibited the alpha-adrenergic-induced increase of L-type calcium currents. CONCLUSION We found a new binding partner, ENH1, and a new target, alpha1C, for PKD1 in neonatal rat cardiomyocytes. We propose a model where ENH1 scaffolds PKD1 to alpha1C in order to form a signalling complex that regulates the activity of cardiac L-type voltage-gated Ca(2+) channels.
Detection and analysis of Galileo signal in space anomalies from 2017 to 2018

The state of the signal in space (SIS) broadcasted by the broadcast ephemeris will affect the reliability and safety of the user navigation and positioning. Understanding the SIS anomaly of a satellite is conducive to fault allocation in advanced receiver autonomous integrity monitoring. In this study, precise ephemeris was used to calculate the SIS error of all healthy Galileo satellites from 2017 to 2018, the detection thresholds were obtained through statistics of SIS error, and the satellite SIS anomalies for two years were detected. Results show that the SIS anomalies of Galileo satellites are primarily caused by satellite clock error, the scale of which can sometimes reach tens or even hundreds of metres. Except for E24 satellite, the annual accumulation of anomalies for Galileo satellites was <∼7 h, and the average annual accumulation of anomalies is ∼2.5 h. The accumulation of anomalies for E24 in 2017 was ∼50 h∼50 h, but this decreased to 4.5 h in 2018, indicating that its stability improved. Overall, the SIS stability in 2018 was improved compared with 2017. In addition, as the serious missing of broadcast ephemeris of Galileo satellites, it is not conducive to the continuity and reliability of the user's navigation and positioning.
Models of polymer solutions in electrified jets and solution blowing

Fluid flows hosting electrical phenomena make the subject of a fascinating and highly interdisciplinary scientific field. In recent years, the extraordinary success of electrospinning and solution blowing technologies for the generation of polymer nanofibers has motivated vibrant research aiming at rationalizing the behavior of viscoelastic jets under applied electric fields or other stretching fields including gas streams. Theoretical models unveiled many original aspects in the underpinning physics of polymer solutions in jets, and provided useful information to improve experimental platforms. This article reviews advances in the theoretical description and numerical simulation of polymer solution jets in electrospinning and solution blowing. Instability phenomena of electrical and hydrodynamic origin are highlighted, which play a crucial role in the relevant flow physics. Specifications leading to accurate and computationally viable models are formulated. Electrohydrodynamic modeling, theories for the jet bending instability, recent advances in Lagrangian approaches to describe the jet flow, including strategies for dynamic refinement of simulations, and effects of strong elongational flow on polymer networks are reviewed. Finally, the current challenges and future perspectives of the field are outlined and discussed, including the task of correlating the physics of the jet flows with the properties of realized materials, as well as the development of multiscale techniques for modelling viscoelastic jets.
Working Memory as Endophenotypes in First-Degree Relatives of Children with Neurodevelopmental Disorders: An Indian Account

Few studies from India have undertaken research on neurocognitive endophenotypes in neurodevelopmental disorders (NDD). Hence, the researchers’objective was to assess one of the criterion of a neurocognitive endophenotype -heritability. This study assesses an association between the children with neurodevelopmental disorders (NDD) and their firstdegree relatives (FDR)--parents in working memory (WM) performance, using a framework of a unified definition of NDD. Additionally, differences in gender of parents in WM performance were assessed. A two-group cross-sectional design with 42 probands of NDD and their 54 parents (both mothers and fathers) were assessed on a similar WM battery of tests. Correlation between probands and parents and between mothers and fathers yielded no significant differences in their respective WM performance. The need for better statistical, methodological measures in the hands of an isolated researcher was highlighted along with the discussion in light of the concept of an endophenotype.
Changes in enzymatic activities during “koji” incubation and natural fermentation of soybean paste

Soybean paste production requires two steps “koji” incubation and natural fermentation. During the incubation of the koji, the protease activity was gradually increased and the highest activity was obtained on 30th day. Neutral protease was the predominant protease and it was significantly increased during the incubation of the koji. The nitrogen solubility index, nonprotein nitrogen (NPN), aminophenol nitrogen, and peptides concentration were gradually increased and they were 69.07%, 42.07%, 3.73%, and 38.34%, respectively, at the end of the fermentation process. The activities of α-amylase, glucoamylase, and cellulase have different changing periods. All enzymes showed a significant increase followed by a gradual decrease to reach a linear shape at later koji incubation times. During the moromi fermentation, all enzymes showed a gradual decrease. This result is of immense importance for understanding the enzymatic mechanism involved and improving the final product by acting at different stages along the fermentation process. Practical applications Soybean paste production requires two steps “koji” incubation and natural fermentation. This study aims to understand changes in enzymatic activities during soybean paste production. This result is of immense importance for understanding the enzymatic mechanism involved in the fermentation process and supposed to improve the quality of final products. It may help to improve the final product by acting at different stages along the fermentation process.
Superhydrophobic Cellulose Paper‐Based Triboelectric Nanogenerator for Water Drop Energy Harvesting

The high‐efficiency conversion of water drop mechanical energy into electrical energy has always been an urgent issue in the development and utilization of raindrop energy. In this work, a novel drum‐like triboelectric nanogenerator (D‐TENG) with robust self‐cleaning superhydrophobic features is developed to harvest water drop energy. An elastic superhydrophobic cellulose paper is created by spray‐coating nanofumed silica dispersed in a thermoplastic elastomer solution, followed by treatment with triethoxy‐1H,1H,2H,2H‐tridecafluoro‐n‐octylsilane. When raindrops hit the D‐TENG surface, the superhydrophobic cellulose paper will vibrate and periodic contact and separation with polytetrafluoroethylene will occur to generate electricity. The results demonstrate that when a 6 mm water drop falls from a height of 2.5 m and hits the D‐TENG, the generated voltage output can reach a peak of 21.6 V and charge transfer of 10 nC. The output power of the D‐TENG can reach 16 µW per droplet, which is more than 13.3 times that generated from the previous TENGs based on the electrostatic induction of water droplets. These results indicate that the superhydrophobic cellulose paper‐based D‐TENG is potentially a strong candidate for harvesting energy from raindrops, thereby making it a promising sustainable energy source for next‐generation electronics.
Knowledge Transfer through Mobile Application Needs Questionnaire: Validity and Reliability

Introduction: Knowledge transfer through mobile application needs assessment to develop a user-friendly technology. However, the means to measure the need is unavailable. An instrument was developed from the Joanna Briggs new model of EBP to examine the nurses’ needs. This study aims to examine the reliability and validity of knowledge transfer through mobile application needs questionnaire for nurses. Methods: The validity of the questionnaire was examined for its content with Content Validity Index (CVI). CVI was measured for its relevance, ambiguity, clarity and simplicity on each item (I-CVI) and scale (S-CVI). Content validity assessment forms and guidelines were given to six experts in nursing and computer sciences. Their suggestions contributed to the questionnaire revision. Subsequently, the online questionnaire was completed by 40 nurses randomly selected in a hospital in Depok City. Internal consistency was analyzed for the reliability. Results: The CVI of this questionnaire is high with mean of I-CVI and S-CVI at 0.96 relevance, 0.98 ambiguity, 0.97 clarity and 0.94 simplicity. Cronbach’s alpha shows that the internal consistency is acceptable (0.87). Conclusion: The questionnaire is content valid and reliable. This questionnaire can be used to measure those needs and contribute to the design of a user-friendly mobile application.
A method to evaluate task-specific importance of spatio-temporal units based on explainable artificial intelligence

ABSTRACT Big geo-data are often aggregated according to spatio-temporal units for analyzing human activities and urban environments. Many applications categorize such data into groups and compare the characteristics across groups. The intergroup differences vary with spatio-temporal units, and the essential is to identify the spatio-temporal units with apparently different data characteristics. However, spatio-temporal dependence, data variety, and the complexity of tasks impede an effective unit assessment. Inspired by the applications to extract critical image components based on explainable artificial intelligence (XAI), we propose a spatio-temporal layer-wise relevance propagation method to assess spatio-temporal units as a general solution. The method organizes input data into an extensible three-dimensional tensor form. We provide two means of labeling the spatio-temporal tensor data for typical geographical applications, using temporally or spatially relevant information. Neural network training proceeds to extract the global and local characteristics of data for corresponding analytical tasks. Then the method propagates classification results backward into units as obtained task-specific importance. A case study with taxi trajectory data in Beijing validates the method. The results prove that the proposed method can evaluate the task-specific importance of spatio-temporal units with dependence. This study also attempts to discover task-related knowledge using XAI.
Electrochemical Sensors Coupled with Multivariate Statistical Analysis as Screening Tools for Wine Authentication Issues: A Review

Consumers are increasingly interested in the characteristics of the products they consume, including aroma, taste, and appearance, and hence, scientific research was conducted in order to develop electronic senses devices that mimic the human senses. Thanks to the utilization of electroanalytical techniques that used various sensors modified with different electroactive materials coupled with pattern recognition methods, artificial senses such as electronic tongues (ETs) are widely applied in food analysis for quality and authenticity approaches. This paper summarizes the applications of electrochemical sensors (voltammetric, amperometric, and potentiometric) coupled with unsupervised and supervised pattern recognition methods (principal components analysis (PCA), linear discriminant analysis (LDA), partial least square (PLS) regression, artificial neural network (ANN)) for wine authenticity assessments including the discrimination of varietal and geographical origins, monitoring the ageing processes, vintage year discrimination, and detection of frauds and adulterations. Different wine electrochemical authentication methodologies covering the electrochemical techniques, electrodes types, functionalization sensitive materials and multivariate statistical analysis are emphasized and the main advantages and disadvantages of using the proposed methodologies for real applications were concluded.
Invisible Stories That Drive Online Social Cognition

Detection of online subversive activities, such as fake news, concerted campaigns, and bots, is getting increasingly urgent. However, without specific knowledge of underlying facts and disparate valid perspectives of a given issue, it is hard to detect subversive intent in a generic sense. To address this, we approach the problem from a “macro” perspective. Rather than asking whether a specific social media account is acting subversively, we look at the entire discourse around a trending topic, and ask whether the discourse looks “healthy” or is it showing signs of getting hijacked or dominated by one particular perspective. To do this, we break down a social discourse into its constituent narratives. Narratives are in turn modeled as latent stories or worldviews, whose visible characterizations are in the form of specific distributions over different opinions expressed in the discourse. Once the discourse is broken down into narratives, the “health” of the discourse can be addressed using various measures, such as the relative sizes of its constituent narratives, sentiment polarity of internarrative interactions, and presence or absence of dominant players within each narrative. We conduct experiments on several well-known trending topics on Twitter to identify its constituent narratives and provide a report card on the overall discourse quality. We also show how this top–down approach offers the means to delineate roles played by users as drivers of the discourse, the constituent narratives, or their component opinions, determined on the basis of dominance centrality measures and narrative affinities.
Relations between Parental Autonomy Support and Coercion with Children’s Total Difficulties

Child-rearing practices can either negatively or positively influence the psychosocial outcomes of their children. Negative psychological outcomes such as total difficulties can result in peer relationship problem, hyperactivity, emotional symptoms, and conduct problem among children. Thus, this study aims to examine the relations between parental autonomy support and coercion with total difficulties of children in Malaysia. As respondents of this study, a total of 502 children aged between 9 and 12 years were recruited using a multistage proportionate-to-size sampling technique. The descriptive analysis found that 8.4% of respondents experienced an abnormal level of total difficulties score that can result in mental disorder. The results indicated that children who perceived a higher level of autonomy support from parents experienced a lower level of total difficulties. In contrast, children who perceived their parents as coercive reported more total difficulties. The results indicated that autonomy support from parents is vital in protecting their children during middle childhood from behavioral, emotional, and peer relationship difficulties. Parental coercion tends to intensify total difficulties. Thus, to help their children in mitigating the performance of total difficulties, parents should be empowered with positive parenting skills. Hubungan antara Dukungan Otonomi Orangtua (Parental Autonomy Support), Koersif dan Kesulitan Total pada Anak