Miracle-dz/UniFAR-FTUs huggingface dataset:
Detoxification of Reactive Carbonyl Species by Glutathione Transferase Tau Isozymes

Oxidative stimuli to living cells results in the formation of lipid peroxides, from which various aldehydes and ketones (oxylipin carbonyls) are inevitably produced. Among the oxylipin carbonyls, those with an α,β-unsaturated bond are designated as reactive carbonyl species (RCS) because they have high electrophilicity and biological activity. Plants have arrays of dehydrogenases and reductases to metabolize a variety of RCS that occur in the cells, but these enzymes are not efficient to scavenge the most toxic RCS (i.e., acrolein) because they have only low affinity. Two glutathione transferase (GST) isozymes belonging to the plant-specific Tau class were recently observed to scavenge acrolein with KM values at a submillimolar level. This suggests that GST could also be involved in the defense system against RCS. We tested the activities of 23 Tau isozymes of Arabidopsis thaliana for five types of RCS, and the results revealed that 11 isozymes recognized either acrolein or 4-hydroxy-(E)-2-nonenal or both as a substrate(s). Such RCS-scavenging activities indicate the potential contribution of GST to RCS scavenging in plants, and they may account for the stress tolerance conferred by several Tau isozymes. RCS are therefore a strong candidate for endogenous substrates of plant GSTs.
Atrial Transcriptional Profiles of Molecular Targets Mediating Electrophysiological Function in Aging and Pgc-1β Deficient Murine Hearts

Background Deficiencies in the transcriptional co-activator, peroxisome proliferative activated receptor, gamma, coactivator-1β are implicated in deficient mitochondrial function. The latter accompanies clinical conditions including aging, physical inactivity, obesity, and diabetes. Recent electrophysiological studies reported that Pgc-1β-/- mice recapitulate clinical age-dependent atrial pro-arrhythmic phenotypes. They implicated impaired chronotropic responses to adrenergic challenge, compromised action potential (AP) generation and conduction despite normal AP recovery timecourses and background resting potentials, altered intracellular Ca2+ homeostasis, and fibrotic change in the observed arrhythmogenicity. Objective We explored the extent to which these age-dependent physiological changes correlated with alterations in gene transcription in murine Pgc-1β-/- atria. Methods and Results RNA isolated from murine atrial tissue samples from young (12–16 weeks) and aged (>52 weeks of age), wild type (WT) and Pgc-1β-/- mice were studied by pre-probed quantitative PCR array cards. We examined genes encoding sixty ion channels and other strategic atrial electrophysiological proteins. Pgc-1β-/- genotype independently reduced gene transcription underlying Na+-K+-ATPase, sarcoplasmic reticular Ca2+-ATPase, background K+ channel and cholinergic receptor function. Age independently decreased Na+-K+-ATPase and fibrotic markers. Both factors interacted to alter Hcn4 channel activity underlying atrial automaticity. However, neither factor, whether independently or interactively, affected transcription of cardiac Na+, voltage-dependent K+ channels, surface or intracellular Ca2+ channels. Nor were gap junction channels, β-adrenergic receptors or transforming growth factor-β affected. Conclusion These findings limit the possible roles of gene transcriptional changes in previously reported age-dependent pro-arrhythmic electrophysiologial changes observed in Pgc-1β-/- atria to an altered Ca2+-ATPase (Atp2a2) expression. This directly parallels previously reported arrhythmic mechanism associated with p21-activated kinase type 1 deficiency. This could add to contributions from the direct physiological outcomes of mitochondrial dysfunction, whether through reactive oxygen species (ROS) production or altered Ca2+ homeostasis.
Principal elliptic bundles and compact homogeneous l.c.K. manifolds

In this paper, we provide a complete description of Vaisman structures on certain principal elliptic bundles over complex flag manifolds, this description allows us to classify homogeneous l.c. K. structures on compact homogeneous Hermitian manifolds. Moreover, as an application of the latter result, through these structures we explicitly describe all homogeneous Hermitian metrics which solve the Hermitian-Einstein-Weyl equation on compact homogeneous Hermitian-Weyl manifolds. The main feature of our approach is to employ elements of representation theory of simple Lie algebras to describe Cartan-Ehresmann connections on principal elliptic bundles over complex flag manifolds. Further, we also present a characterization for Kahler covering of compact homogeneous l.c. K. manifolds as regular locus of certain Stein spaces (with isolated singularity) defined by the complex analytification of HV-varieties, a.k. a. Kostant cones. This characterization of Kahler covering allows us to describe the Kodaira-type embedding for compact homogeneous l.c. K. manifolds purely in terms of elements of representation theory of semisimple complex algebraic groups.
Analytical calculation of optical forces on spherical particles in optical tweezers: tutorial

Recently, the Nobel prize in physics was awarded for the invention of optical tweezers. Thus far, they have been used as tools to obtain measurements of microscopic systems, mainly by harnessing the optical forces. For quite some time, it has been used as a tool to measure microscopic systems, mainly due to optical forces. These optical forces are commonly deduced from assumptions based on the particle size with respect to the trapping laser wavelength. However, a complete electromagnetic model is accurate and does not depend on early approximations of the force model. Further, the model has several advantages, such as morphology-dependent resonances, size dependence for large spheres, and multipole effects from smaller particles, just to name a few. In this tutorial, we review and discuss the physical modeling of optical forces in optical tweezers, which are the resultant forces exerted by a trapping beam on a sphere of any size and composition. We discuss the highly focused Gaussian beam for trapping and also provide a description of the generalization for any other beam type.
Using Videos to Evaluate Image Model Robustness

Human visual systems are robust to a wide range of image transformations that are challenging for artificial networks. We present the first study of image model robustness to the minute transformations found across video frames, which we term "natural robustness". Compared to previous studies on adversarial examples and synthetic distortions, natural robustness captures a more diverse set of common image transformations that occur in the natural environment. Our study across a dozen model architectures shows that more accurate models are more robust to natural transformations, and that robustness to synthetic color distortions is a good proxy for natural robustness. In examining brittleness in videos, we find that majority of the brittleness found in videos lies outside the typical definition of adversarial examples (99.9\%). Finally, we investigate training techniques to reduce brittleness and find that no single technique systematically improves natural robustness across twelve tested architectures.
Acoustic scene classification using teacher-student learning with soft-labels

Acoustic scene classification identifies an input segment into one of the pre-defined classes using spectral information. The spectral information of acoustic scenes may not be mutually exclusive due to common acoustic properties across different classes, such as babble noises included in both airports and shopping malls. However, conventional training procedure based on one-hot labels does not consider the similarities between different acoustic scenes. We exploit teacher-student learning with the purpose to derive soft-labels that consider common acoustic properties among different acoustic scenes. In teacher-student learning, the teacher network produces soft-labels, based on which the student network is trained. We investigate various methods to extract soft-labels that better represent similarities across different scenes. Such attempts include extracting soft-labels from multiple audio segments that are defined as an identical acoustic scene. Experimental results demonstrate the potential of our approach, showing a classification accuracy of 77.36 % on the DCASE 2018 task 1 validation set.
The Fungal CYP51s: Their Functions, Structures, Related Drug Resistance, and Inhibitors

CYP51 (Erg11) belongs to the cytochrome P450 monooxygenase (CYP) superfamily and mediates a crucial step of the synthesis of ergosterol, which is a fungal-specific sterol. It is also the target of azole drugs in clinical practice. In recent years, researches on fungal CYP51 have stepped into a new stage attributing to the discovery of crystal structures of the homologs in Candida albicans, Cryptococcus neoformans and Aspergillus fumigatus. This review summarizes the functions, structures of fungal CYP51 proteins, and the inhibitors targeting these homologs. In particular, several drug-resistant mechanisms associated with the fungal CYP51s are introduced. The sequences and crystal structures of CYP51 proteins in different fungal species are also compared. These will provide new insights for the advancement of research on antifungal agents.
Read classification using semi-supervised deep learning

In this paper, we propose a semi-supervised deep learning method for detecting the specific types of reads that impede the de novo genome assembly process. Instead of dealing directly with sequenced reads, we analyze their coverage graphs converted to 1D-signals. We noticed that specific signal patterns occur in each relevant class of reads. Semi-supervised approach is chosen because manually labelling the data is a very slow and tedious process, so our goal was to facilitate the assembly process with as little labeled data as possible. We tested two models to learn patterns in the coverage graphs: M1+M2 and semi-GAN. We evaluated the performance of each model based on a manually labeled dataset that comprises various reads from multiple reference genomes with respect to the number of labeled examples that were used during the training process. In addition, we embedded our detection in the assembly process which improved the quality of assemblies.
Boosting Vehicle-to-Cloud Communication by Machine Learning-Enabled Context Prediction

The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. However, the massive increases in Machine-type Communication (MTC) highly stress the capacities of the network infrastructure. With the system-immanent limitation of resources in cellular networks and the resource competition between human cell users and MTC, more resource-efficient channel access methods are required in order to improve the coexistence of the different communicating entities. In this paper, we present a machine learning-enabled transmission scheme for client-side opportunistic data transmission. By considering the measured channel state as well as the predicted future channel behavior, delay-tolerant MTC is performed with respect to the anticipated resource-efficiency. The proposed mechanism is evaluated in comprehensive field evaluations in public Long Term Evolution (LTE) networks, where it is able to increase the mean data rate by 194% while simultaneously reducing the average power consumption by up to 54%.
Estimating Risk Levels of Driving Scenarios through Analysis of Driving Styles for Autonomous Vehicles

In order to operate safely on the road, autonomous vehicles need not only to be able to identify objects in front of them, but also to be able to estimate the risk level of the object in front of the vehicle automatically. It is obvious that different objects have different levels of danger to autonomous vehicles. An evaluation system is needed to automatically determine the danger level of the object for the autonomous vehicle. It would be too subjective and incomplete if the system were completely defined by humans. Based on this, we propose a framework based on nonparametric Bayesian learning method -- a sticky hierarchical Dirichlet process hidden Markov model(sticky HDP-HMM), and discover the relationship between driving scenarios and driving styles. We use the analysis of driving styles of autonomous vehicles to reflect the risk levels of driving scenarios to the vehicles. In this framework, we firstly use sticky HDP-HMM to extract driving styles from the dataset and get different clusters, then an evaluation system is proposed to evaluate and rank the urgency levels of the clusters. Finally, we map the driving scenarios to the ranking results and thus get clusters of driving scenarios in different risk levels. More importantly, we find the relationship between driving scenarios and driving styles. The experiment shows that our framework can cluster and rank driving styles of different urgency levels and find the relationship between driving scenarios and driving styles and the conclusions also fit people's common sense when driving. Furthermore, this framework can be used for autonomous vehicles to estimate risk levels of driving scenarios and help them make precise and safe decisions.
Single-photon computational 3D imaging at 45  km

Long-range active imaging has a variety of applications in remote sensing and target recognition. Single-photon LiDAR (light detection and ranging) offers single-photon sensitivity and picosecond timing resolution, which is desirable for high-precision three-dimensional (3D) imaging over long distances. Despite important progress, further extending the imaging range presents enormous challenges because only weak echo photons return and are mixed with strong noise. Herein, we tackled these challenges by constructing a high-efficiency, low-noise confocal single-photon LiDAR system, and developing a long-range-tailored computational algorithm that provides high photon efficiency and super-resolution in the transverse domain. Using this technique, we experimentally demonstrated active single-photon 3D-imaging at a distance of up to 45 km in an urban environment, with a low return-signal level of $\sim$1 photon per pixel. Our system is feasible for imaging at a few hundreds of kilometers by refining the setup, and thus represents a significant milestone towards rapid, low-power, and high-resolution LiDAR over extra-long ranges.
Self-testing of quantum systems: a review

Self-testing is a method to infer the underlying physics of a quantum experiment in a black box scenario. As such it represents the strongest form of certification for quantum systems. In recent years a considerable self-testing literature has developed, leading to progress in related device-independent quantum information protocols and deepening our understanding of quantum correlations. In this work we give a thorough and self-contained introduction and review of self-testing and its application to other areas of quantum information.
Gamete Nuclear Migration in Animals and Plants

The migration of male and female gamete nuclei to each other in the fertilized egg is a prerequisite for the blending of genetic materials and the initiation of the next generation. Interestingly, many differences have been found in the mechanism of gamete nuclear movement among animals and plants. Female to male gamete nuclear movement in animals and brown algae relies on microtubules. By contrast, in flowering plants, the male gamete nucleus is carried to the female gamete nucleus by the filamentous actin cytoskeleton. As techniques have developed from light, electron, fluorescence, immunofluorescence, and confocal microscopy to live-cell time-lapse imaging using fluorescently labeled proteins, details of these differences in gamete nuclear migration have emerged in a wide range of eukaryotes. Especially, gamete nuclear migration in flowering plants such as Arabidopsis thaliana, rice, maize, and tobacco has been further investigated, and showed high conservation of the mechanism, yet, with differences among these species. Here, with an emphasis on recent developments in flowering plants, we survey gamete nuclear migration in different eukaryotic groups and highlight the differences and similarities among species.
A survey on Big Data and Machine Learning for Chemistry

Herein we review aspects of leading-edge research and innovation in chemistry which exploits big data and machine learning (ML), two computer science fields that combine to yield machine intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. But the potential benefits of ML come at the cost of big data production; that is, the algorithms, in order to learn, demand large volumes of data of various natures and from different sources, from materials properties to sensor data. In the survey, we propose a roadmap for future developments, with emphasis on materials discovery and chemical sensing, and within the context of the Internet of Things (IoT), both prominent research fields for ML in the context of big data. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to chemistry, outlining processes, discussing pitfalls, and reviewing cases of success and failure.
Flower and Pod Source Influence on Pea Weevil (Bruchus pisorum) Oviposition Capacity and Preference

Bruchus pisorum is an insect pest causing major damage to pea seeds worldwide. Control is difficult and limited resistance is available. In this work we studied the effects of pollen and pod source on insect fecundity and oviposition by comparing resistant and susceptible Pisum spp. accessions and non-host (Lathyrus sativus and Vicia faba) species. A first no-choice assay revealed that the source of flower offered to adults for feeding might retard oviposition (the case of V. faba), reduce fertility (Pisum sativum ssp. syriacum, P. fulvum, and V. faba) or increase adult mortality (V. faba and P. sativum ssp. syriacum). A second no-choice assay with all adults fed with pollen of the same pea cultivar showed significant effect of the source of pods offered. Oviposition was reduced on pods of some resistant Pisum accessions, but particularly low on pods of the non-hosts, being retarded if ever happening and coupled with high mortality of adults. This was confirmed in a third experiment consisting on dual-choice assays showing reduced egg laying in V. faba, L. sativus, P. fulvum, and P. sativum ssp. syriacum compared to the commercial variety pea used as a control (Messire).
High temperature induced B1 to B2 phase transformation in Cd doped ZnO nanocomposite thin films

In this paper, we report the high temperature mediated structural phase transformation from B1 to B2 phase in solgel derived Cd0.4Zn0. 6O nanocomposite thin film for the temperature range of 700 to 900 degree centigrade. Xray diffraction pattern provides a direct and unambiguous indication of phase evolution with increase in annealing Temperature from 700 to 900 degree centigrade along with coexistence of wurtzite phase throughout the whole transformation regime. In fact, significant reduction in lattice parameters in case of transformed B2 phase, extracted by Rietveld refinement provides a direct indication of chemical pressure inducement inside the lattice owing to anticipated mismatch in thermal expansion coefficient which leads to facilitate such phase transformation. Raman spectrum for 900 degree centigrade annealed ones, moreover, shows a distinguishable phonon mode at 866 centimeter inverse, suggesting the formation of B2 phase at higher temperature, consistent with XRD results. Scanning electron microscopy reveals the agglomeration of nanocrystallites followed by distinct void formation, indicating the evolution of chemical pressure inside the lattice. Soft Xray absorption measurement reflects a deviation of the inflection point of the absorption edge which implies Fermi level pinning with transforming phase.
Effects of Carbon Pore Size on the Contribution of Ionic Liquid Electrolyte Phase Transitions to Energy Storage in Supercapacitors

Recent research on ionic liquid electrolyte-based supercapacitors indicated the contribution of phase transitions of the electrolytes at high cell voltages to the energy stored. This mechanism can be exploited to significantly increase the energy density of supercapacitors, which up to now remains their major drawback. It was found that these ordering transitions require the presence of mesopores within the carbon electrode materials and that porosity in general is a key factor to trigger them, but details of the mechanism remain unexplained. To get a more profound understanding of this phenomenon, carbon materials with different pore diameters and volumes were synthesized and the effect of those properties on the phase transitions in the ionic liquids was studied by means of cyclic voltammetry. A clear correlation between the peak current and the mesopore volume is revealed and an optimal pore diameter was determined, exceeding which does not improve the phase transition behavior. These findings are useful as guidelines for the rational design of carbon mesopores in order to utilize the new energy storage modes which are neither fully capacitive, nor redox-based.
Research on the Effects of Entrepreneurial Education and Entrepreneurial Self-Efficacy on College Students’ Entrepreneurial Intention

Entrepreneurship is one of the important engines of economic development. Under the influence of policy encouragement and economic situation, college students have become the emerging entrepreneurial subjects. Studying the factors influencing their willingness to innovate is conducive to improving the entrepreneurial status and performance. From the perspective of planned behavior theory, this paper analyzes the effects of college students’ entrepreneurship education and self-efficacy on their entrepreneurial intention. Using a sample of 327 college students in China, we test the hypotheses, and get some results. Firstly, college students’ entrepreneurial education has a significant positive effect on their entrepreneurial intention, but has no obvious effect on the entrepreneurial attitude. Secondly, college students’ entrepreneurial self-efficacy has a significant positive effect on the entrepreneurial attitude and entrepreneurial intention, and the entrepreneurial attitude plays a partial intermediary role in the relationship between entrepreneurial self-efficacy and entrepreneurial intention.
Novel 4-Acrylamido-Quinoline Derivatives as Potent PI3K/mTOR Dual Inhibitors: The Design, Synthesis, and in vitro and in vivo Biological Evaluation

A novel structural series of quinoline derivatives were designed, synthesized and biologically evaluated as PI3K/mTOR dual inhibitors upon incorporation of C-4 acrylamide fragment. Consequently, all of them exerted remarkable inhibition against PI3Kα with IC50 values ranging from 0.50 to 2.03 nM. Besides, they exhibited sub-micromolar to low micromolar anti-proliferative activity against both prostate cancer PC3 and colorectal cancer HCT116 cell lines. In subsequent profiling, 8i, a representative compound throughout this series, also significantly inhibited other class I PI3Ks and mTOR. In PC3 cells, it remarkably down-regulated the crucial biomarkers of PI3K/Akt/mTOR signaling, including phos-Akt (Ser473), phos-Akt (Thr308), phos-S6 ribosomal protein (Ser235/236), and phos-4E-BP1 (Thr37/46), at a concentration as low as 5 nM. Moreover, 8i displayed favorable metabolic stability with long elimination half-life in both human liver and rat liver microsomes. A further in vivo pharmacokinetic (PK) study demonstrated 8i possessed acceptable oral exposure, peak plasma concentration, and elimination half-life. Taken together, 8i, as a potent PI3K/mTOR dual inhibitor, merited further investigation and structural optimization.