Miracle-dz/UniFAR-FTUs huggingface dataset:
Output from Linear Generator for VIV-driven Buoys

Abstract : A derivation of a non-dimensionalized math model was presented for predicting oscillation frequencies of a submerged tethered buoy. The model included a prediction of the buoy excitation frequencies due to vortex shedding. To match this excitation frequency with buoy natural frequencies, this model included coupling effects of an internal pendulum and added mass of the buoy, but excluded viscous damping. This model did not include coupling of the buoy motion on the vortex shedding frequencies. This report also presents a linear generator system that could be used internally in an upright buoy to produce power. Simple power tests were conducted using machine and manual actuation of the magnets. Future work on this project will include (1) laboratory and field testing to validate the VIV oscillation model, (2) a possible adjustment of the model to include a coupling effect of buoy motion with vortex shedding as well as viscous damping, and (3) incorporation of a generator into a buoy design to validate power output predictions. The effects of the generator on the model will the then be included.
On the theory and application of pattern maximum likelihood

Pattern Maximum Likelihood (PML) is a method of probability estimation that works well for large alphabets. It does not assume that all elements from the unknown alphabet have been observed. PML outperforms the traditional Maximum Likelihood for sequences, and it is particularly useful when the sample size is small. In this dissertation we study both the theory and application of PML. For the theory part, we extend the previous results on the properties of PML, and also show how to find the PML distributions analytically for patterns of simple forms. For general patterns, PML probabilities can be approximated using a previously developed EM algorithm, which we will prove to be equivalent to a generalized Gradient Ascend Method. We also use the algorithm to conduct experiments on different distributions and evaluate the performance of PML. In addition, we investigate the calculation of pattern probability. We show that the pattern probability is closely related to symmetric polynomials, and it can be written as a summation over graphs using power sums. Along the way we reveal a relation between pattern probability and the enumeration of certain connected graphs as well as inversion-free trees. For applications, we show how PML can be used to predict the number of new symbols that would appear in a future sample. We conduct experiments on various distributions and compare PML to the method of Good & Toulmin and the method of Efron & Thisted. We demonstrate that PML outperforms the other methods even if the future sample size is large. Finally we apply PML to authenticating the authorship of the Taylor poem, attributed to Shakespeare, and conclude that it is consistent with Efron and Thisted's models. PML deals with samples from a single distribution. In the last part of this dissertation we extend PML to set-patterns where multiple samples are observed from concurrent Bernoulli processes. Analogous to the single-process patterns, we show that for certain forms of set-patterns we can find the exact Set-pattern Maximum Likelihood (SPML) probabilities analytically. Furthermore, for general set- patterns we extend the previous EM algorithm to approximate the SPML probabilities. We also show that for samples taken from Poisson distributions the set-pattern is reduced to the single-process pattern problem
Renormalization scheme dependence of high-order perturbative QCD predictions

Conventionally, one adopts typical momentum flow of a physical observable as the renormalization scale for its perturbative QCD (pQCD) approximant. This simple treatment leads to renormalization scheme-and-scale ambiguities due to the renormalization scheme and scale dependence of the strong coupling and the perturbative coefficients do not exactly cancel at any fixed order. It is believed that those ambiguities will be softened by including more higher-order terms. In the paper, to show how the renormalization scheme dependence changes when more loop terms have been included, we discuss the sensitivity of pQCD prediction on the scheme parameters by using the scheme-dependent $\{\beta_{m \geq 2}\}$-terms. We adopt two four-loop examples, $e^+ e^- \to {\rm hadrons}$ and $\tau$ decays into hadrons, for detailed analysis. Our results show that under the conventional scale setting, by including more-and-more loop terms, the scheme dependence of the pQCD prediction cannot be reduced as efficiently as that of the scale dependence. Thus a proper scale-setting approach should be important to reduce the scheme dependence. We observe that the principle of minimum sensitivity could be such a scale-setting approach, which provides a practical way to achieve optimal scheme and scale by requiring the pQCD approximate be independent to the "unphysical" theoretical conventions.
Human-Machine Collaboration in Decision-Making

The use of ML-based decision support systems in busi-ness-related decision-making processes is a proven approach for companies to increase process performance and quality. To a certain extent, machines are capable of reproducing the cognitive intelligence of humans in specific domains. In order to leverage the resulting potential, synergistic human-machine collaboration (HMC) is becoming increasingly important for companies. However, orchestrating HMC is dependent on a set of framework conditions that determine the success of the collaboration. This study 1 examines the research question of how to utilize the concept of collaborative intelligence (CI) to enhance decision-making processes while using machine learning (ML) -based data prediction. The purpose is to identify success factors in the development, design, and implementation of an ML-based predictive analytics so-lution to orchestrate HMC in decision-making processes. These success factors state recommendations for companies to fulfil the necessary framework conditions for synergetic HMC orchestration. In total, five success factors were identified that represent a combination of theoretical findings and empirical insights. At the same time, further research needs were uncovered, which point out starting points for future research projects in the field of HMC.
Estimation of peculiar velocity from the inverse Tully–Fisher relation

We present a method for deriving a smoothed estimate of the peculiar velocity field of a set of galaxies with measured circular velocities $\eta\equiv {\rm log} \Delta v$ and apparent magnitudes $m$. The method is based on minimizing the scatter of a linear inverse Tully-Fisher relation $\eta= \eta(M)$ where the absolute magnitude of each galaxy is inferred from its redshift $z$, corrected by a peculiar velocity field, $M \propto m - 5\log(z-u)$. We describe the radial peculiar velocity field $u({\bf z})$ in terms of a set of orthogonal functions which can be derived from any convenient basis set; as an example we take them to be linear combinations of low order spherical harmonic and spherical Bessel functions. The model parameters are then found by maximizing the likelihood function for measuring a set of observed $\eta$. The predicted peculiar velocities are free of Malmquist bias in the absence of multi-streaming, provided no selection criteria are imposed on the measurement of circular velocities. This procedure can be considered as a generalized smoothing algorithm of the peculiar velocity field, and is particularly useful for comparison to the smoothed gravity field derived from full-sky galaxy redshift catalogs such as the IRAS surveys. We demonstrate the technique using a catalog of ``galaxies" derived from an N-body simulation. Increasing the resolution of the velocity smoothing beyond a certain level degrades the correlation of fitted velocities against the velocities calculated from linear theory methods, which have finite resolution,
Theoretical Interpretation of Magnetic Activity

Magnetic fields generated and driven by thermal convection are the primary cause of solar activity. There are many facets of the activity, such as plages, flares, sunspots, coronal heating, and the variation of solar luminosity or irradiance whose nature and cause are understood only partially or not at all, although detailed superficial observational descriptions are available. It is suggested that the inferred 10 gauss azimuthal field bundles may be a direct result of the emergence of Q-loops to form bipolar magnetic regions on the surface in association with an increase in solar irradiance.
Loss of linker histone H1 in the maternal genome influences DEMETER-mediated demethylation and affects the endosperm DNA methylation landscape

The Arabidopsis DEMETER (DME) DNA glycosylase demethylates the central cell genome prior to fertilization. This epigenetic reconfiguration of the female gamete companion cell establishes gene imprinting in the endosperm and is essential for seed viability. DME demethylates small and genic-flanking transposons as well as intergenic and heterochromatin sequences, but how DME is recruited to these target loci remains unknown. H1.2 was identified as a DME-interacting protein in a yeast two-hybrid screen, and maternal genome H1 loss affects DNA methylation and expression of selected imprinted genes in the endosperm. Yet, the extent to which how H1 influences DME demethylation and gene imprinting in the Arabidopsis endosperm has not been investigated. Here, we showed that unlike in the vegetative cell, both canonical histone H1 variants are present in the central cell. Our endosperm methylome analysis revealed that without the maternal linker histones, DME-mediated demethylation is facilitated, particularly in the heterochromatin regions, indicating that H1-containing nucleosomes are barriers for DME demethylation. Loss of H1 in the maternal genome has a very limited effect on gene transcription or gene imprinting regulation in the endosperm; however, it variably influences euchromatin TE methylation and causes a slight hypermethylation and a reduced expression in selected imprinted genes. We conclude that loss of maternal H1 indirectly influences DME-mediated demethylation and endosperm DNA methylation landscape but does not appear to affect endosperm gene transcription and overall imprinting regulation.
Modifications in the piperazine ring of nucleozin affects anti-influenza activity

The infection caused by influenza virus is a latent tret, to contribute on the advancement of the discovery and design of nucleozin analogs, a molecule with antiinfluenza activity, we analyzed nucleozin analogs with modifications in the piperazine system, which leads to molecules with larger conformational freedom. Following a new nucleozin synthetic strategy, we obtained three new nucleozin analogs, and two of them were biologically evaluated in vitro and were less active than nucleozin. The loss of activity in the more flexible molecules highlights the need for the piperazine ring to maintain the activity of nucleozin analogues. Interestingly, this coincides with a QSAR model developed here for the prediction of the anti-influenza activity. The proposed model, along with the synthetic route, will be useful for further development of nucleozin analogues with antiviral activity.
Engineered synthetic one-carbon fixation exceeds yield of the Calvin Cycle

One-carbon (C1) feedstocks derived from CO2 and renewable electricity, such as formate, are promising substrates for sustainable production of chemicals, food and fuels. Energetically more efficient, engineered C1-fixation pathways were proposed to increase biomass yields above their natural counterparts, but have so far not been shown to achieve this. Here, we replace the native ‘energy-inefficient’ Calvin-Benson-Bassham (CBB) cycle in Cupriavidus necator by genomic integration of the synthetic reductive glycine pathway for growth on formate. Our final engineered strain reaches a higher biomass yield than the CBB-cycle-utilizing wild type, showing for the first time that efficiencies found in natural metabolism can be exceeded via a synthetic pathway. This yield increase demonstrates the potential of synthetic metabolism and is an important step towards realizing truly sustainable, economically feasible bio-based production.
Persistent acetylation of histone H3 lysine 56 compromises the activity of DNA replication origins

In Saccharomyces cerevisiae, newly synthesized histone H3 are acetylated on lysine 56 (H3 K56ac) by the Rtt109 acetyltransferase prior to their deposition on nascent DNA behind replication forks. Two deacetylases of the sirtuin family, Hst3 and Hst4, remove H3 K56ac from chromatin following S phase. hst3Δ hst4Δ cells present constitutive H3 K56ac, which sensitizes cells to replicative stress via mechanisms that remain unclear. We performed a screen to identify genes that influence cell fitness upon nicotinamide (NAM)-induced inhibition of sirtuins. The screen revealed that DBF4 heterozygosity causes NAM sensitivity. DBF4 and CDC7 encode subunits of the Dbf4-dependent kinase, which activates origins of DNA replication. We show that i) cells harboring the dbf4-1 or cdc7-4 hypomorphic alleles are sensitive to NAM, ii) Rif1, an inhibitor of Cdc7-dependent activation of origins, causes DNA damage and replication defects in NAM-treated cells and hst3Δ hst4Δ mutants, and iii) cdc7-4 hst3Δ hst4Δ cells display synthetic temperature sensitivity associated with delayed initiation of DNA replication. Such replication defects are not due to activation of the intra-S phase checkpoint but require Rtt109-dependent H3 K56ac. Overall, these results suggest that persistent H3 K56ac sensitizes cells to replicative stress in part by negatively influencing replication origin activity.
3D genome topologies distinguish pluripotent epiblast and primitive endoderm cells in the mouse blastocyst

The development of embryonic cell lineages is tightly controlled by transcription factors that regulate gene expression and chromatin organisation. To investigate the specialisation of 3D genome structure in pluripotent or extra-embryonic endoderm lineages, we applied Genome Architecture Mapping (GAM) in embryonic stem (ES) cells, extra-embryonic endoderm (XEN) stem cells, and in their in vivo counterparts, the epiblast (Epi) and primitive endoderm (PrE) cells, respectively. We discover extensive differences in 3D genome topology including the formation domain boundaries that differ between Epi and PrE lineages, both in vivo and in vitro, at lineage commitment genes. In ES cells, Sox2 contacts other active regions enriched for NANOG and SOX2 binding sites. PrE-specific genes, such as Lama1 and Gata6, form repressive chromatin hubs in ES cells. Lama1 activation in XEN or PrE cells coincides with its extensive decondensation. Putative binding sites for OCT4 and SNAIL, or GATA4/6, distinguish chromatin contacts unique to embryonic or extra-embryonic lineages, respectively. Overall, 3D genome folding is highly specialised in early development, especially at genes encoding factors driving lineage identity. Highlights ES and XEN cells have specialised 3D genome structures GAM applied in the blastocyst distinguishes Epi and PrE genome structures Lineage specific genes establish cell-type specific chromatin contacts Specific chromatin contacts feature putative bindings sites for GATA4/6 in XEN cells and SNAIL in ES cells
Characterization of novel Actinobacteriophage Giantsbane reveals unexpected cluster AU relationships

Bacteriophages that infect Arthrobacter, a genus of bacteria which play key ecological roles in soil, warrant further study. Giantsbane, a novel Actinobacteriophage, was isolated using Arthrobacter globiformis as a host. Transmission electron microscopy and whole-genome sequencing revealed a Siphoviridae morphology and a genome length of 56,734 bp. Genome annotation identified 94 putative genes, such as a duplicated major tail protein and a major capsid and protease fusion protein. No genes were associated with lysogeny, indicating a lytic phage. Giantsbane was assigned to the phage cluster AU. Batch average nucleotide identity analysis and phylogenetic networks constructed from shared genes revealed unexpected nucleotide and gene content similarities within cluster AU. These findings have resulted in the creation of two new AU subclusters and the resubclustering of three AU bacteriophages. Analysis using Phamerator and MEME identified repeated motifs and a gene cassette present in all evaluated cluster AU phages which may promote recombination. These findings offer the first intra-cluster analysis of cluster AU phages and further our understanding of the relationships between closely related bacteriophages.
Microbial Gene Ontology informed deep neural network for microbe functionality discovery in human diseases

Background The human microbiome plays a crucial role in human health and is associated with a number of human diseases. Determining microbiome functional roles in human diseases remains a biological challenge due to the high dimensionality of metagenome gene features. However, existing models were limited in providing biological interpretability, where the functional role of microbes in human diseases is unexplored. Here we propose to utilize a neural network-based model incorporating Gene Ontology (GO) relationship network to discover the microbe functionality in human diseases. Results We use four benchmark datasets, including diabetes, liver cirrhosis, inflammatory bowel disease, and colorectal cancer, to explore the microbe functionality in the human diseases. Our model discovered and visualized the novel candidates’ important microbiome genes and their functions by calculating the important score of each gene and GO term in the network. Furthermore, we demonstrate that our model achieves a competitive performance in predicting the disease by comparison with other non-Gene Ontology informed models. Conclusions The discovered candidates’ important microbiome genes and their functions provide novel insights into microbe functional contribution.
SARS-CoV-2 infected cells sprout actin-rich filopodia that facilitate viral invasion

Emerging COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a great threat to human health and economics. Although SARS-CoV-2 entry mechanism has been explored, little is known about how SARS-CoV-2 regulates the host cell remodeling to facilitate virus invasion process. Here we unveil that SARS-CoV-2 boosts and repurposes filopodia for entry to the target cells. Using SARS-CoV-2 virus-like particle (VLP), real-time live-cell imaging and simulation of active gel model, we reveal that VLP-induced Cdc42 activation leads to the formation of filopodia, which reinforce the viral entry to host cells. By single-particle tracking and sparse deconvolution algorithm, we uncover that VLP particles utilize filopodia to reach the entry site in two patterns, ‘surfing’ and ‘grabbing’, which are more efficient and faster than entry via flat plasma membrane regions. Furthermore, the entry process via filopodia is dependent on the actin cytoskeleton and actin-associated proteins fascin, formin, and Arp2/3. Importantly, either inhibition the actin cross-linking protein fascin or the active level of Cdc42 could significantly hinders both the VLP and the authentic SARS-CoV-2 entry. Together, our results highlight that the spatial-temporal regulation of the actin cytoskeleton by SARS-CoV-2 infection makes filopodia as a ‘highway’ for virus entry, which emerges as an antiviral target. Significance Statement Revealing the mechanism of SARS-CoV-2 invasion is of great significance to explain its high pathogenic and rapid transmission in the world. We discovered a previously unknown route of SARS-CoV-2 entry. SARS-CoV-2 virus-like particles boost cellular filopodia formation by activating Cdc42. Using state-of-art-technology, we spatial-temporally described how virus utilize filopodia to enter the target cell in two modes: ‘surfing’ and ‘grabbing’. Filopodia can directly transport the virus to endocytic hot spots to avoid the virus from disorderly searching on the plasma membrane. Our study complements current knowledge of SARS-CoV-2 that filopodia and its components not only play an important role in virus release and cell-cell transmission, but also in the entry process, and provides several potential therapeutic targets for SARS-CoV-2. Highlights SARS-CoV-2 VLP infection promotes filopodia formation by activating Cdc42 SARS-CoV-2 VLP utilizes filopodia to enter target cell via two modes, ‘surfing’ and ‘grabbing’ Filopodia disruption compromises the invasion of both VLP and authentic SARS-CoV-2
MEASURING SUCCESS OF COMMERCE SYSTEMS IN VIRTUAL WORLDS

: Recently, there has been an advent of virtual worlds like Second Life and Entropia Universe; however little is published on these worlds, the virtual goods and their commerce systems. In this paper central elements of virtual worlds are discussed. In a further step, a classification of virtual goods is proposed. Based on the updated DeLone and McLean IS Success Model and their application to eCommerce systems, we adapted and identified success measures for commerce systems in virtual worlds. A total of 43 success measures are discussed and classified according to their success dimensions. The resulting measures are then applied to two case examples showing the respective success model of each commerce system.
Learning with minimal effort: leveraging in silico labeling for cell and nucleus segmentation

Deep learning provides us with powerful methods to perform nucleus or cell segmentation with unprecedented quality. However, these methods usually require large training sets of manually annotated images, which are tedious and expensive to generate. In this paper we propose to use In Silico Labeling (ISL) as a pretraining scheme for segmentation tasks. The strategy is to acquire label-free microscopy images (such as bright-field or phase contrast) along fluorescently labeled images (such as DAPI or CellMask). We then train a model to predict the fluorescently labeled images from the label-free microscopy images. By comparing segmentation performance across several training set sizes, we show that such a scheme can dramatically reduce the number of required annotations.
The Quest of Cost-effective Models Detecting Depression from Speech

In this work, we explore the effectiveness of two different acoustic feature groups 1 - conventional hand-curated and deep representation features, for predicting the 2 severity of depression from speech. We measure the relevance of possible contribut-3 ing factors to the models’ performance, including gender of the individual, severity 4 of the disorder, content and length of speech. Our findings suggest that models 5 trained on conventional acoustic features perform equally well or better than the 6 ones trained on deep representation features at significantly lower computational 7 cost, irrespective of other factors, e