Miracle-dz/UniFAR-FTUs huggingface dataset:
Metformin Improves Diabetic Bone Health by Re-Balancing Catabolism and Nitrogen Disposal

Objective Metformin, a leading drug used to treat diabetic patients, is reported to benefit bone homeostasis under hyperglycemia in animal models. However, both the molecular targets and the biological pathways affected by metformin in bone are not well identified or characterized. The objective of this study is to investigate the bioengergeric pathways affected by metformin in bone marrow cells of mice. Materials and Methods Metabolite levels were examined in bone marrow samples extracted from metformin or PBS -treated healthy (Wild type) and hyperglycemic (diabetic) mice using liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. We applied an untargeted high performance LC-MS approach which combined multimode chromatography (ion exchange, reversed phase and hydrophilic interaction (HILIC)) and Orbitrap-based ultra-high accuracy mass spectrometry to achieve a wide coverage. A multivariate clustering was applied to reveal the global trends and major metabolite players. Results A total of 346 unique metabolites were identified, and they are grouped into distinctive clusters that reflected general and diabetes-specific responses to metformin. As evidenced by changes in the TCA and urea cycles, increased catabolism and nitrogen waste that are commonly associated with diabetes were rebalanced upon treatment with metformin. In particular, we found glutamate and succinate whose levels were drastically elevated in diabetic animals were brought back to normal levels by metformin. These two metabolites were further validated as the major targets of metformin in bone marrow stromal cells. Conclusion Overall using limited sample size, our study revealed the metabolic pathways modulated by metformin in bones which have broad implication in our understanding of bone remodeling under hyperglycemia and in finding therapeutic interventions in mammals.
The Asymptotic Dynamics of two-dimensional (anti-)de Sitter Gravity

We show that the asymptotic dynamics of two-dimensional de Sitter or anti-de Sitter Jackiw-Teitelboim (JT) gravity is described by a generalized two-particle Calogero-Sutherland model. This correspondence is established by formulating the JT model of (A)dS gravity in two dimensions as a topological gauge theory, which reduces to a nonlinear 0+1-dimensional sigma model on the boundary of (A)dS space. The appearance of cyclic coordinates allows then a further reduction to the Calogero-Sutherland quantum mechanical model.
Mobility helps peer-to-peer security

We propose a straightforward technique to provide peer-to-peer security in mobile networks. We show that far from being a hurdle, mobility can be exploited to set up security associations among users. We leverage on the temporary vicinity of users, during which appropriate cryptographic protocols are run. We illustrate the operation of the solution in two scenarios, both in the framework of mobile ad hoc networks. In the first scenario, we assume the presence of an offline certification authority and we show how mobility helps to set up security associations for secure routing; in this case, the security protocol runs over one-hop radio links. We further show that mobility can be used for the periodic renewal of vital security information (e.g., the distribution of hash chain/Merkle tree roots). In the second scenario, we consider fully self-organized security: Users authenticate each other by visual contact and by the activation of an appropriate secure side channel of their personal device; we show that the process can be fuelled by taking advantage of trusted acquaintances. We then show that the proposed solution is generic: It can be deployed on any mobile network and it can be implemented either with symmetric or with asymmetric cryptography. We provide a performance analysis by studying the behavior of the solution in various scenarios.
A simplified power loss calculation method for PFC boost topologies

In this paper, a novel, simple and accurate method is proposed to predict the RMS and average current for each component in the most common continuous conduction mode (CCM) AC-DC power factor correction (PFC) boost derived topologies. The model is based on using the effective duty cycle independent of the switching action. The proposed model enables simple and accurate estimation of powertrain component conduction losses. The paper includes the derivation of the RMS, or average current for the boost and interleaved boost PFC topologies. PSIM simulation and experimental results are used to verify the accuracy of model. Experimental and simulation results of a prototype interleaved boost converter converting universal AC input voltage to 400 V DC at up to 3.4 kW output are given to verify the proposed model. The experimental results demonstrate that the model can correctly predict the RMS and average currents in the interleaved boost topology.
A Developmental Venous Anomaly Presenting Atypical Findings on Susceptibility-Weighted Imaging

Developmental venous anomaly (DVA) is the most common type of vascular malformation found at autopsy, with a prevalence of 2%. [1][1] DVA is composed of radially arranged venous complexes converging to a centrally located venous trunk, which drains the normal brain parenchyma. [2][2] Susceptibility-
Some Theorems on Polygons with One-line Spectral Proofs

We use discrete Fourier transforms and convolution products to give one-line proofs of some theorems about planar polygons. We illustrate the method by computing the perspectors of a pair of concentric equilateral triangles constructed from a hexagon and leave the proofs of Napoleon’s theorem, the Barlotti theorem, the Petr–Douglas–Neumann theorem, and other theorems as an exercise.
MEMORIZATION PRECEDES GENERATION: LEARNING UNSUPERVISED GANS

We propose an approach to address two undesired properties of unsupervised GANs. First, since GANs use only a continuous latent distribution to embed multiple classes or clusters of a dataset, GANs often do not correctly handle the structural discontinuity between disparate classes in a latent space. Second, discriminators of GANs easily forget about past generated samples by generators, incurring instability during adversarial training. We argue that these two infamous problems of unsupervised GANs can be largely alleviated by a memory structure to which both generators and discriminators can access. Generators can effectively store a large amount of training samples that are needed to understand the underlying cluster distribution, which eases the structure discontinuity problem. At the same time, discriminators can memorize previously generated samples, which mitigate the forgetting problem. We propose a novel end-to-end GAN model named memoryGAN, that involves a memory network that can be trained in an unsupervised manner, and integrable to many existing models of GANs. With evaluations on multiple datasets including Fashion-MNIST, CelebA, CIFAR10, and Chairs, we show that our model is probabilistically interpretable, and generates image samples of high visual fidelity. We also show that our memoryGAN also achieves the state-of-the-art inception scores among unsupervised GAN models on the CIFAR10 dataset, without additional tricks or weaker divergences.
Adaptive Evolution Towards Predicted Optimal Growth in Escherichia coli K-12

Whole cell metabolic networks of prokaryotic cells can be reconstructed using genomic, biochemical and physiological data. Constraints-based in silico models of these networks can then be utilized to calculate optimal cellular growth rates on common carbon substrates that are consistent with experimental data. Herein, adaptive evolution of Escherichia coli K-12 MG1655 resulted in an optimal growth rate that was predicted a priori. In particular, the growth rate of E. coli on glycerol evolved from a sub-optimum to the predicted optimal growth rate over 40 days or about 700 generations. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis.
Recovery characteristics of NBTI of pMOSFETs with oxynitride dielectrics under drain bias

In this paper, the recovery characteristics of negative bias temperature instability (NBTI) of pMOSFETs under drain bias were studied. It is observed that, the drain bias not only worsens the NBTI degradation in high |Vds| region but also suppresses the recovery ratio of NBTI. The time evolutions of recovery show that the drain bias dependent NBTI recovery is mainly related to the fast recovery effect in initial 1s. While in long time scale, the recovery obeys power law dependence on time. A physical model based on donor-type interface traps neutralization at the beginning of recovery is proposed to explain the suppressed recovery ratio under drain bias.
Associative Measures and Multi-word Unit Extraction in Turkish

Associative measures are “mathematical formulas determining the strength of association between two or more words based on their occurrences and cooccurrences in a text corpus” (Pecina, 2010, p. 138). The purpose of this paper is to test the 12 associative measures that Text-NSP (Banerjee & Pedersen, 2003) contains on a 10-million-word subcorpus of Turkish National Corpus (TNC) (Aksan et.al., 2012). A statistical comparison of those measures is out of the scope of the study, and the measures will be evaluated according to the linguistic relevance of the rankings they provide. The focus of the study is basically on optimizing the corpus data, before applying the measures and then, evaluating the rankings produced by these measures as a whole, not on the linguistic relevance of individual n-grams. The findings include intra-linguistically relevant associative measures for a comma representative, 10-million-word corpus of Turkish. splitted, delimited, sentence lower-cased, well-balanced
High Risk Population Isolate Reveals Low Frequency Variants Predisposing to Intracranial Aneurysms

3% of the population develops saccular intracranial aneurysms (sIAs), a complex trait, with a sporadic and a familial form. Subarachnoid hemorrhage from sIA (sIA-SAH) is a devastating form of stroke. Certain rare genetic variants are enriched in the Finns, a population isolate with a small founder population and bottleneck events. As the sIA-SAH incidence in Finland is >2× increased, such variants may associate with sIA in the Finnish population. We tested 9.4 million variants for association in 760 Finnish sIA patients (enriched for familial sIA), and in 2,513 matched controls with case-control status and with the number of sIAs. The most promising loci (p<5E-6) were replicated in 858 Finnish sIA patients and 4,048 controls. The frequencies and effect sizes of the replicated variants were compared to a continental European population using 717 Dutch cases and 3,004 controls. We discovered four new high-risk loci with low frequency lead variants. Three were associated with the case-control status: 2q23.3 (MAF 2.1%, OR 1.89, p 1.42×10-9); 5q31.3 (MAF 2.7%, OR 1.66, p 3.17×10-8); 6q24.2 (MAF 2.6%, OR 1.87, p 1.87×10-11) and one with the number of sIAs: 7p22.1 (MAF 3.3%, RR 1.59, p 6.08×-9). Two of the associations (5q31.3, 6q24.2) replicated in the Dutch sample. The 7p22.1 locus was strongly differentiated; the lead variant was more frequent in Finland (4.6%) than in the Netherlands (0.3%). Additionally, we replicated a previously inconclusive locus on 2q33.1 in all samples tested (OR 1.27, p 1.87×10-12). The five loci explain 2.1% of the sIA heritability in Finland, and may relate to, but not explain, the increased incidence of sIA-SAH in Finland. This study illustrates the utility of population isolates, familial enrichment, dense genotype imputation and alternate phenotyping in search for variants associated with complex diseases.
Detailed Response to Reviewers

We present a multi-analytical study on the formulation of commercial felt-tip pens, introduced in the second half of the 20th century and commonly used by modern and contemporary artists. These media of both drawing and writing have not yet been fully investigated, but the degradation processes they might undergo, such as fading, are well-known and rather apparent. Twelve water-based felt-tip pens were investigated by the joint use of complementary analytical techniques, such as Thin Layer Chromatography, Nuclear Magnetic Resonance, Fourier transformed Infrared spectroscopy, X-Ray Fluorescence spectrometry and Pyrolysis-Gas Chromatography/Mass Spectrometry. The obtained results provided crucial, preliminary data for the identification of dyes, solvents and additives present in the inks' formulations. Numerous synthetic food colouring agents and pigments were identified, such as Acid yellow 23, Acid Red 18, Acid Blue 9, Pigment Blue 15. In addition, glycols, fatty acids, 2-phenoxyethanol, colophony, benzotriazole derivatives and other solvents and additives were detected in the manufactured inks. Furthermore, the effects of photo-degradation on one emblematic ink sample were studied, highlighting in particular visual and aesthetical changes due to discolouration. This study demonstrates a methodology based upon the use of an integrated analytical approach for the characterisation of commercial ink-based artistic media and their viable degradation patterns, which aims to develop suitable conservation treatments to assess modern and contemporary drawings and writings.
EXACT ELECTROMAGNETIC FIELD EXCITED BY A VERTICAL MAGNETIC DIPOLE ON THE SURFACE OF A LOSSY HALF-SPACE

A rigorous analytical procedure is developed that allows the exact evaluation of the complete integral representations for the time-harmonic electromagnetic (EM) fleld components generated by a vertical magnetic dipole (VMD) lying on the surface of a ∞at and homogeneous lossy half-space. Closed-form expressions for the radial distributions of the EM fleld components induced on the surface of the half-space are provided in terms of exponential functions and modifled Bessel functions. Such expressions make it possible to overcome the limitations implied by the previously published quasi-static solutions, which are valid only in the low-frequency range. Numerical results are presented to show where the quasi-static approximations deviate from the exact solutions for a given homogeneous medium as frequency is changed. The computed amplitude and phase frequency spectra of the EM fleld components demonstrate that the quasi-static approach produces inaccurate results at frequencies higher than 1MHz, and that, in particular, it leads to underestimating the EM fleld strength. Finally, it is also shown that at a frequency equal to or greater than 10MHz excellent results in terms of accuracy may be obtained by using the high-frequency asymptotic forms of the exact solutions.
Alessandro Codivilla and the first pancreatoduodenectomy.

In the field of pancreatic surgery, probably no operation has engendered more historic debate than the origin of the pancreatoduodenectomy, with the misleading and arguably inappropriate eponyms of the “Whipple” or the “Kausch-Whipple” procedure. The question ofwhoperformedthefirstpancreatoduodenectomyremainsanintriguingonethathasbeen debated from a nationalistic approach as well as a historic one, but data confirming these fervent arguments have often been missing; thus, we felt the need for clarification of the unrecorded but true story of the first pancreatoduodenectomy.
A γ-Strawman privacy-preserving scheme in weighted social networks

With the dramatic development of social network applications, such as the Facebook, Twitter, and MySpace, privacy-preserving problem is getting increasingly concerned. Apart from node information, researchers have found that structure information can also leak data providers' privacy, especially in weighted social networks. However, most of the existing private-preserving schemes focus on a single aspect. The comprehensive consideration introduces two challenges. On one hand, the different anonymity demands of node and structure information lead to the collision of different design criteria, which is called as consistency matching problem. On the other hand, the simple combination of existing schemes may introduce large amounts of unnecessary changes, which makes the published information meaningless. Thus, we must find the balance between anonymity demands and changes, which is called as optimization trade-off problem. In this paper, we propose a γ-Strawman privacy-preserving scheme in weighted social networks to solve these challenges. To address consistency matching problem, we propose a greedy algorithm based on a user trade-off metric. For optimization tradeoff problem, a closeness edge-editing technology is considered, which can change the private information slightly. Finally, we evaluate our scheme on real-world datasets, the experimental results show that the γ-Strawman scheme is efficient. Copyright © 2017 John Wiley & Sons, Ltd.
Deconvoluting lung evolution: from phenotypes to gene regulatory networks

Speakers in this symposium presented examples of respiratory regulation that broadly illustrate principles of evolution from whole organ to genes. The swim bladder and lungs of aquatic and terrestrial organisms arose independently from a common primordial “respiratory pharynx” but not from each other. Pathways of lung evolution are similar between crocodiles and birds but a low compliance of mammalian lung may have driven the development of the diaphragm to permit lung inflation during inspiration. To meet the high oxygen demands of flight, bird lungs have evolved separate gas exchange and pump components to achieve unidirectional ventilation and minimize dead space. The process of “screening” (removal of oxygen from inspired air prior to entering the terminal units) reduces effective alveolar oxygen tension and potentially explains why nonathletic large mammals possess greater pulmonary diffusing capacities than required by their oxygen consumption. The “primitive” central admixture of oxygenated and deoxygenated blood in the incompletely divided reptilian heart is actually co-regulated with other autonomic cardiopulmonary responses to provide flexible control of arterial oxygen tension independent of ventilation as well as a unique mechanism for adjusting metabolic rate. Some of the most ancient oxygen-sensing molecules, i.e., hypoxia-inducible factor-1alpha and erythropoietin, are up-regulated during mammalian lung development and growth under apparently normoxic conditions, suggesting functional evolution. Normal alveolarization requires pleiotropic growth factors acting via highly conserved cell–cell signal transduction, e.g., parathyroid hormone-related protein transducing at least partly through the Wingless/int pathway. The latter regulates morphogenesis from nematode to mammal. If there is commonality among these diverse respiratory processes, it is that all levels of organization, from molecular signaling to structure to function, co-evolve progressively, and optimize an existing gas-exchange framework.
Under-determined Training and Estimation for Distributed Transmit Beamforming Systems

Distributed transmit beamforming (DTB) can significantly boost the signal-to-noise ratio (SNR) of a wireless communication system. To realize the benefits of DTB, generating and feeding back beamforming vector are very challenging tasks. Existing schemes have either enormous overhead or weak robustness in noisy channels. In this paper, we investigate the design of training sequences and beamforming vector estimators in DTB systems. We consider an under-determined case, where the length of training sequence N sent from each node is smaller than the number of source nodes M. We derive the optimal estimation of the beamforming vector that maximizes the beamforming gain and show that it can be well approximated as the linear minimum mean square error (LMMSE) estimator. Based on the LMMSE estimator, we investigate the optimal design of training sequences and propose efficient DTB schemes. We analytically show that these schemes can achieve approximately N times increased SNR in uncorrelated channels, and even higher gain in correlated ones. We also propose a concatenated training scheme which optimally combines the training signals over multiple frames to obtain the beamforming vector. Simulation results demonstrate that the proposed DTB schemes can yield significant gains even at very low SNRs, with total feedback bits much less than those required in the existing schemes.
The MicSim Package of R: An Entry-Level Toolkit for Continuous-Time Microsimulation

High entry barriers might discourage many social scientists from using existing microsimulation software. This article presents the functionality and capabilities of the free and open source R package MicSim which allows performing continuous-time microsimulation at a very fine-grained level from a low level entry point. Hence, the package might also be of interest to people who are new to R. The package implements a generic microsimulation model for a wide range of demographic applications. Its central device is the individual life-course, which is defined by the sequence of states the individual visits over time and the intervals between the transitions from one state to another. The MicSim model is by design a discrete-event simulation model in which a demographic event implies a change in the state of an individual. The considered population is open and life-courses are specified to evolve along three continuous time scales: individual age, calendar time, and the time that an individual has already spent in the current state. The stochastic model of the microsimulation is parameterized with a base population, transition rates, and (if applicable) a population of migrants. Besides stochastic events, the package allows specifying deterministic events such as enrollment of children in elementary school. Example applications illustrate the capabilities of the package.
Machine Classification of Peer Comments in Physics

As part of an ongoing project where SWoRD, a Web-based reciprocal peer review system, is used to support disciplinary writing, this study reports machine learning classifications of student comments on peer writing collected in the SWoRD system. The student comments on technical lab reports were first manually decomposed and coded as praise, criticism, problem detection, solution suggestion, summary, or off-task. Then TagHelper 2.0 was used to classify the codes, using three frequently used algorithms: Naive Bayes, Support Vector Machine, and a Decision Tree. It was found that Support Vector machine performed best in terms of Cohen’s Kappa.