Miracle-dz/UniFAR-FTUs huggingface dataset:
DIFFERENTIATION OF HUMAN GERM CELL TUMOR CELLS IN VIVO AND IN VITRO

Three in vitro human germ cell tumor cell lines with different cell lineage and variable capability of differentiation were established from testicular germ cell tumors. NCR-G1 corresponding to yolk sac tumor were induced α-fetoprotein (AFP) production with retinoic acid (RA) treatment. NCR-G2 and -G3 were human embryonal carcinoma (EC) cell lines. They have cytological and immuno-phenotypic characteristics common to human EC cells as has been described in other EC cells. NCR-G2 grew as floating cell aggregates in the culture medium, whereas NCR-G3 grew as floating cell aggregate and flattered cells attached to the surface of culture dish. These flattened cells of NCR-G3 immunohistochemically expressed a variety of differentiation antigens including myogenic markers, extraembryonic trophoblastic cell marker (human chorionic gonadotropin (hCG)), and extracellular proteins. Differentiation capabilities of NCR-G3 cells became more evident when they were treated with RA. By flowcytometric analysis, human EC cell surface markers 2D7, 2H2 and 5D4 which were mouse monoclonal antibodies we obtained from immunization by NCR-G2 cells, disappeared from cell surface of RA-treated NCR-G3 cells. Consistent with these findings, the flattened cells of NCR-G3 that attached to the culture dish were negative for these antibodies. Moreover, longer exposure to RA enhanced hCG production in exposure time dependent fashion. These observations clearly indicate that NCR-G3 possess multipotent differentiation capabilities and they differentiated into the trophoblastic cell lineage other than somatic cells. In contrast to NCR-G3, NCR-G2 did not show any differentiation capabilities with RA treatment. All three germ cell tumor lines produced tumors in athymic mice with 100% efficiency. High AFP content was observed in the sera of NCR-G1 tumor-bearing mice. High hCG and AFP contents were detected in the sera of the NCR-G3 tumor-bearing mice. Thus, our newly established human germ cell tumor cell lines add new insights of molecular mechanism of human embryogenesis and differentiation of EC cells.
Genetic Variation in Rare and Common Plants

▪ Abstract Isozyme variation in 247 plant species is summarized as 57 generic-level comparisons of rare and common species. All species-level measures of variation (Ps, As, APs, Hes) and mean population-level measures (Pp, Ap, APp, Hep, and Ho) show reductions significant at the p < 0.001 level, but FIS and FST did not differ significantly, reflecting the similarity of breeding system in congeneric species and disparate ranges often sampled for rare and common species. The reduction in gene flow (Nm) among populations of rare species was significant when estimated from FST, but not when estimated from private alleles. Species monomorphic for isozymes are predominantly endemic and self-fertile. Although census populations of virtually all rare species are higher than levels at which theory would predict genetic erosion, and higher than levels protected by the U.S. Endangered Species Act (ESA), rare plants evidently have more significant reductions in genetic variation and gene flow than have been recognize.
Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G

Owing to the large volume of sensed data from the enormous number of IoT devices in operation today, centralized machine learning algorithms operating on such data incur an unbearable training time, and thus cannot satisfy the requirements of delay-sensitive inference applications. By provisioning computing resources at the network edge, Mobile Edge Computing (MEC) has become a promising technology capable of collaborating with distributed IoT devices to facilitate federated learning, and thus realize real-time training. However, considering the large volume of sensed data and the limited resources of both edge servers and IoT devices, it is challenging to ensure the training efficiency and accuracy of delay-sensitive training tasks. Thus, in this paper, we design a novel edge computing-assisted federated learning framework, in which the communication constraints between IoT devices and edge servers and the effect of various IoT devices on the training accuracy are taken into account. On one hand, we employ machine learning methods to dynamically configure the communication resources in real-time to accelerate the interactions between IoT devices and edge servers, thus improving the training efficiency of federated learning. On the other hand, as various IoT devices have different training datasets which have varying influence on the accuracy of the global model derived at the edge server, an IoT device selection scheme is designed to improve the training accuracy under the resource constraints at edge servers. Extensive simulations have been conducted to demonstrate the performance of the introduced edge computing-assisted federated learning framework.
An instanton-like excitation of a discrete time crystal

Spontaneous symmetry breaking and elementary excitation are two of the pillars of condensed matter physics that are closely related to each other. The symmetry and its spontaneous breaking not only control the dynamics and spectrum of elementary excitations, but also determine their underlying structures. In this paper, we study the excitation properties of a non-equilibrium quantum matter: a discrete time crystal phase that spontaneously breaks the temporal translational symmetry. It is shown that such an intriguing symmetry breaking allows an instanton-like excitation that represents a tunneling between two "degenerate" time crystal phases. Furthermore, we also observe a dynamical transition point at which the instanton "size" diverges, a reminiscence of the critical slowing down phenomenon in nonequilibrium statistic physics. A phenomenological theory has been proposed to understand the phase dynamics of the proposed system and the experimental realization and detection have also been discussed.
Differential Affinities of Visual Arrestin, βArrestin1, and βArrestin2 for G Protein-coupled Receptors Delineate Two Major Classes of Receptors*

Visual arrestin, βarrestin1, and βarrestin2 comprise a family of intracellular proteins that desensitize G protein-coupled receptors (GPCRs). In addition, βarrestin1 and βarrestin2 target desensitized receptors to clathrin-coated pits for endocytosis. Whether arrestins differ in their ability to interact with GPCRs in cells is not known. In this study, we visualize the interaction of arrestin family members with GPCRs in real time and in live cells using green fluorescent protein-tagged arrestins. In the absence of agonist, visual arrestin and βarrestin1 were found in both the cytoplasm and nucleus of HEK-293 cells, whereas βarrestin2 was found only in the cytoplasm. Analysis of agonist-mediated arrestin translocation to multiple GPCRs identified two major classes of receptors. Class A receptors (β2 adrenergic receptor, mu opioid receptor, endothelin type A receptor, dopamine D1A receptor, and α1b adrenergic receptor) bound βarrestin2 with higher affinity than βarrestin1 and did not interact with visual arrestin. In contrast, class B receptors (angiotensin II type 1A receptor, neurotensin receptor 1, vasopressin V2 receptor, thyrotropin-releasing hormone receptor, and substance P receptor) bound both βarrestin isoforms with similar high affinities and also interacted with visual arrestin. Switching the carboxyl-terminal tails of class A and class B receptors completely reversed the affinity of each receptor for the visual and non-visual arrestins. In addition, exchanging the βarrestin1 and βarrestin2 carboxyl termini reversed their extent of binding to class A receptors as well as their subcellular distribution. These results reveal for the first time marked differences in the ability of arrestin family members to bind GPCRs at the plasma membrane. Moreover, they show that visual arrestin can interact in cells with GPCRs other than rhodopsin. These findings suggest that GPCR signaling may be differentially regulated depending on the cellular complement of arrestin isoforms and the ability of arrestins to interact with other cellular proteins.
DeepInit Phase Retrieval

This paper shows how data-driven deep generative models can be utilized to solve challenging phase retrieval problems, in which one wants to reconstruct a signal from only few intensity measurements. Classical iterative algorithms are known to work well if initialized close to the optimum but otherwise suffer from non-convexity and often get stuck in local minima. We therefore propose DeepInit Phase Retrieval, which uses regularized gradient descent under a deep generative data prior to compute a trained initialization for a fast classical algorithm (e.g. the randomized Kaczmarz method). We empirically show that our hybrid approach is able to deliver very high reconstruction results at low sampling rates even when there is significant generator model error. Conceptually, learned initializations may therefore help to overcome the non-convexity of the problem by starting classical descent steps closer to the global optimum. Also, our idea demonstrates superior runtime performance over conventional gradient-based reconstruction methods. We evaluate our method for generic measurements and show empirically that it is also applicable to diffraction-type measurement models which are found in terahertz single-pixel phase retrieval.
A Refined Deep Learning Architecture for Diabetic Foot Ulcers Detection

Diabetic Foot Ulcers (DFU) that affect the lower extremities are a major complication of diabetes. Each year, more than 1 million diabetic patients undergo amputation due to failure to recognize DFU and get the proper treatment from clinicians. There is an urgent need to use a CAD system for the detection of DFU. In this paper, we propose using deep learning methods (EfficientDet Architectures) for the detection of DFU in the DFUC2020 challenge dataset, which consists of 4,500 DFU images. We further refined the EfficientDet architecture to avoid false negative and false positive predictions. The code for this method is available at this https URL.
Reconfigurable Waveguides Using Glide-Symmetric Bed of Nails: Design of an All-Metal Switch at Millimetre-Wave Band

A reconfigurable millimeter-wave artificial waveguide using glide-symmetric pin-like contact-less metasurfaces is proposed in order to enable low loss and high-power-handling capability in switching operations. Thanks to the remarkable behaviour of glide symmetry, the meta-structures are used both as wide-band EBG material to confine the field in the guide and as low-dispersive guiding medium filling the guide itself. The reconfigurability is achieved by adjusting the displacement between the metasurfaces. A higher displacement enables propagation within the waveguide and a lower displacement suppresses it. A two-state reconfigurability is therefore designed, allowing the device to act as an on-off switch in the V-band. In particular, a dispersion analysis highlights the potential of such technology to achieve switching operating over a 55-66 GHz frequency range. A complete design including matching mechanism and WR15 feeding is also described and shown to exhibit a $S_{11}<-10$ dB bandwidth from 57.4 to 62.8 GHz. In the "off" state, the switch isolation is better than 65 dB and in the "on" state, the insertion losses are better than 0.7 dB within the entire operating frequency range. The isolation between two adjacent waveguides is also discussed to assess the integration of such a device within a switching network.
Generalized Perfect Codes for Symmetric Classical-Quantum Channels

We define a new family of codes for symmetric classical-quantum channels and establish their optimality. To this end, we extend the classical notion of generalized perfect and quasi-perfect codes to channels defined over some finite dimensional complex Hilbert output space. The resulting optimality conditions depend on the channel considered and on an auxiliary state defined on the output space of the channel. For certain $N$ -qubit classical-quantum channels, we show that codes based on a generalization of Bell states are quasi-perfect and, therefore, they feature the smallest error probability among all codes of the same blocklength and cardinality.
A fast noise filtering algorithm for time series prediction using recurrent neural networks

Recent research demonstrate that prediction of time series by recurrent neural networks (RNNs) based on the noisy input generates a smooth anticipated trajectory. We examine the internal dynamics of RNNs and establish a set of conditions required for such behavior. Based on this analysis we propose a new approximate algorithm and show that it significantly speeds up the predictive process without loss of accuracy.
Wikipedia's Network Bias on Controversial Topics

The most important feature of Wikipedia is the presence of hyperlinks in pages. Link placement is the product of people's collaboration, consequently Wikipedia naturally inherits human bias. Due to the high influence that links' disposition has on users' navigation sessions, one needs to verify that, given a controversial topic, the hyperlinks' network does not expose users to only one side of the subject. A Wikipedia's topic-induced network that prevents users the discovery of different facets of an issue, suffers from structural bias. In this work, we define the static structural bias, which indicates if the strength of connections between pages of contrasting inclinations is the same, and the dynamic structural bias, which quantifies the network's level bias that users face over the course of their navigation sessions. Our measurements of structural bias on several controversial topics demonstrate its existence, revealing that users have low likelihood of reaching pages of opposing inclination from where they start, and that they navigate Wikipedia showing a behaviour much more biased than the expected from the baselines. Our findings advance the relevance of the problem and pave the way for developing systems that automatically measure and propose hyperlink locations that minimize the presence and effects of structural bias.
Deep phylogenetic structure has conservation implications for ornate rainbowfish (Melanotaeniidae : Rhadinocentrus ornatus) in Queensland, eastern Australia

The freshwater fish, Rhadinocentrus ornatus Regan, 1914, has a patchy distribution through coastal drainages of Queensland and New South Wales, eastern Australia. Isolated populations of R. ornatus are found on several islands, as well as in a disjunct northern population 350 km from its nearest conspecific population. Deoxyribonucleic acid was extracted and sequenced for the mitochondrial ATPase gene to describe the geographic and genetic subdivision within the species. Four major clades were identified. These clades diverged between two and seven million years ago and so represent long-term divisions and possible units of conservation. There are conservation implications in that the narrow and localised distribution of R. ornatus overlaps with an area of large-scale land clearing, high human population and threats from introduced exotic fish. A particularly high centre of Rhadinocentrus diversity in the Tin Can Bay area of Queensland presents some interesting questions about the evolution of the genus Rhadinocentrus.
Approach for GDPR Compliant Detection of COVID-19 Infection Chains

While prospect of tracking mobile devices' users is widely discussed all over European countries to counteract COVID-19 propagation, we propose a Bloom filter based construction providing users' location privacy and preventing mass surveillance. We apply a solution based on Bloom filters data structure that allows a third party, a government agency, to perform some privacy-preserving set relations on a mobile telco's access logfile. By computing set relations, the government agency, given the knowledge of two identified persons, has an instrument that provides a (possible) infection chain from the initial to the final infected user no matter at which location on a worldwide scale they are. The benefit of our approach is that intermediate possible infected users can be identified and subsequently contacted by the agency. With such approach, we state that solely identities of possible infected users will be revealed and location privacy of others will be preserved. To this extent, it meets General Data Protection Regulation (GDPR)requirements in this area.
openDD: A Large-Scale Roundabout Drone Dataset

Analyzing and predicting the traffic scene around the ego vehicle has been one of the key challenges in autonomous driving. Datasets including the trajectories of all road users present in a scene, as well as the underlying road topology are invaluable to analyze the behavior of the various traffic participants. The interaction between the traffic participants is especially high in intersection types that are not regulated by traffic lights, the most common one being the roundabout. We introduce the openDD dataset, including 84,774 accurately-tracked trajectories and HD map data of seven different roundabouts. The openDD dataset is annotated using images taken by a drone in 501 separate flights, totalling in over 62 hours of trajectory data. As of today the openDD is by far the largest publicly available trajectory dataset recorded from a drone perspective, while comparable datasets span 17 hours at most. The data is available, for both commercial and non-commercial use, at: http://www.13pilot. eu/openDD.
GW190814 as a massive rapidly rotating neutron star with exotic degrees of freedom

In the context of the massive secondary object recently observed in the compact-star merger GW190814, we investigate the possibility of producing massive neutron stars from a few different equation of state models that contain exotic degrees of freedom, such as hyperons and quarks. Our work shows that state-of-the-art relativistic mean-field models can generate massive stars reaching $\ensuremath{\gtrsim}2.05\phantom{\rule{0.16em}{0ex}}{\mathrm{M}}_{\mathrm{Sun}}$, while being in good agreement with gravitational-wave events and x-ray pulsar observations, when quark vector interactions and nonstandard self-vector interactions are introduced. In particular, we present a new version of the Chiral Mean Field (CMF) model in which a different quark-deconfinement potential allows for stable stars with a pure quark core. When rapid rotation is considered, our models generate stellar masses that approach, and in some cases surpass $2.5\phantom{\rule{0.16em}{0ex}}{\mathrm{M}}_{\mathrm{Sun}}$. We find that in such cases fast rotation does not necessarily suppress exotic degrees of freedom due to changes in stellar central density, but require a larger amount of baryons than what is allowed in the nonrotating stars. This is not the case for pure quark stars, which can easily reach $2.5\phantom{\rule{0.16em}{0ex}}{\mathrm{M}}_{\mathrm{Sun}}$ and still possess approximately the same amount of baryons as stable nonrotating stars. We also briefly discuss possible origins for fast rotating stars with a large amount of baryons and their stability, showing how the event GW190814 can be associated with a star containing quarks as one of its progenitors.
Response to selection and genotype–environment interaction in mink (Neovison vison) selected on ad libitum and restricted feeding

Nielsen, V. H. , Moller, S. H. , Hansen, B. K. and Berg, P. 2011. Response to selection and genotype-environment interaction in mink (Neovison vison) selected on ad libitum and restricted feeding. Can. J. Anim. Sci. 91: 231–237. Mink were selected for high November weight (AL line) and low feed conversion ratio (FC line) on ad libitum feeding and for high November weight on restricted feeding (RF line). After three generations of selection, the average estimated breeding value for November weight was 533, 326, and 150 g in males and 168, 82, and −85 g in females in the AL, RF, and FC lines. The breeding value for feed conversion ratio was −1.39, −0.84 and −0.68 kg feed kg−1 gain in males and −0.39, −0.31 and −0.23 kg feed kg−1 gain in females in the selection lines. In generation 4, the AL, RF and FC lines were tested on both ad libitum and restricted feeding. The estimated breeding value for November weight in males in the AL line (533 g) was significantly greater than that in the RF line (384 g) on ad lib.
Static pricing for multi-unit prophet inequalities

Characterizing the Efficiency of Static Pricing Schemes as a Function of the Supply The problem of selling a supply of k units to a stream of customers constitutes one of the cornerstones in revenue management. Static pricing schemes (that output the same price to all customers) are commonly used because of their simplicity and their many desirable properties; they are anonymous, nonadaptive, and order oblivious. Although the efficiency of those schemes should improve as the supply k increases, prior work has only focused either on algorithms that aim for a constant approximation that is independent of k or on the setting where k becomes really large. In contrast, this paper characterizes the efficiency of static pricing schemes as a function of the supply. Our approach stems from identifying a “sweet spot” between selling enough items and obtaining enough utility from customers with high valuations. Subsequent work shows that our pricing scheme is the optimal static pricing for every value of k.
A simple and broadly-applicable definition of shear transformation zones

Plastic deformation in amorphous solids is known to be carried by stress-induced localized rearrangements of a few tens of particles, accompanied by the conversion of elastic energy to heat. Despite their central role in determining how glasses yield and break, the search for a simple and generally-applicable definition of the precursors of those plastic rearrangements -- the so-called `Shear Transformation Zones' (STZs) -- is still ongoing. Here we present a simple definition of STZs -- based solely on the harmonic approximation of a glass' energy. We explain why and demonstrate directly that our proposed definition of plasticity-carriers in amorphous solids is more broadly applicable compared to anharmonic definitions put forward previously. Finally, we offer an open-source library that analyzes low-lying STZs in computer glasses and in laboratory materials such as dense colloidal suspensions for which the harmonic approximation is accessible. Our results constitute a physically-motivated methodological advancement towards characterizing mechanical disorder in glasses, and understanding how they yield.