Miracle-dz/UniFAR-FTUs huggingface dataset:
Structured Illumination in Spatial-Orientational Hyperspace

The dipole nature of chromophore is important for both super-resolution microscopy and imaging molecular structure, which is nevertheless neglected in most microscopies, even including structured illumination microscopy (SIM) with polarized excitations. Here we interpret fluorescent dipoles in spatial-orientational hyperspace, in which polarized excitation is structured illumination on the dimension of dipole orientation. Furthermore, we developed polarized SIM (PSIM), which attains super-resolution imaging of fluorescent dipoles directly on commercial SIM systems.
Quasi-popular Matchings, Optimality, and Extended Formulations

Let [Formula: see text] be an instance of the stable marriage problem in which every vertex ranks its neighbors in a strict order of preference. A matching [Formula: see text] in [Formula: see text] is popular if [Formula: see text] does not lose a head-to-head election against any matching. Popular matchings generalize stable matchings. Unfortunately, when there are edge costs, to find or even approximate up to any factor a popular matching of minimum cost is NP-hard. Let [Formula: see text] be the cost of a min-cost popular matching. Our goal is to efficiently compute a matching of cost at most [Formula: see text] by paying the price of mildly relaxing popularity. Our main positive results are two bicriteria algorithms that find in polynomial time a “quasi-popular” matching of cost at most [Formula: see text]. Moreover, one of the algorithms finds a quasi-popular matching of cost at most that of a min-cost popular fractional matching, which could be much smaller than [Formula: see text]. Key to the other algorithm is a polynomial-size extended formulation for an integral polytope sandwiched between the popular and quasi-popular matching polytopes. We complement these results by showing that it is NP-hard to find a quasi-popular matching of minimum cost and that both the popular and quasi-popular matching polytopes have near-exponential extension complexity.
Pilot-RCT Finds No Evidence for Modulation of Neuronal Networks of Auditory Hallucinations by Transcranial Direct Current Stimulation

Background: Transcranial direct current stimulation (tDCS) is used as treatment for auditory verbal hallucinations (AVH). The theory behind the treatment is that tDCS increases activity in prefrontal cognitive control areas, which are assumed to be hypoactive, and simultaneously decreases activity in temporal speech perception areas, which are assumed to be hyperactive during AVH. We tested this hypofrontal/hypertemporal reversal theory by investigating anatomical, neurotransmitter, brain activity, and network connectivity changes over the course of tDCS treatment. Methods: A double-blind, randomized controlled trial was conducted with 21 patients receiving either sham or real tDCS treatment (2 mA) twice daily for 5 days. The anode was placed over the left dorsolateral prefrontal cortex (DLPFC) and the cathode over the left temporo-parietal cortex (TPC). Multimodal neuroimaging as well as clinical and neurocognitive functioning assessment were performed before, immediately after, and three months after treatment. Results: We found a small reduction in AVH severity in the real tDCS group, but no corresponding neuroimaging changes in either DLPFCD or TPC. Limitations: The study has a small sample size. Conclusion: The results suggest that the currently leading theory behind tDCS treatment of AVH may need to be revised, if confirmed by studies with larger N. Tentative findings point to the involvement of Broca’s area as a critical structure for tDCS treatment.
Rotating skyrmion lattices by spin torques and field or temperature gradients

Chiral magnets like MnSi form lattices of skyrmions, i.e., magnetic whirls, which react sensitively to small electric currents j above a critical current density jc. The interplay of these currents with tiny gradients of either the magnetic field or the temperature can induce a rotation of the magnetic pattern for j$>$jc. Either a rotation by a finite angle of up to 15$irc$ or\textemdash{}for larger gradients\textemdash{}a continuous rotation with a finite angular velocity is induced. We use Landau-Lifshitz-Gilbert equations extended by extra damping terms in combination with a phenomenological treatment of pinning forces to develop a theory of the relevant rotational torques. Experimental neutron scattering data on the angular distribution of skyrmion lattices suggest that continuously rotating domains are easy to obtain in the presence of remarkably small currents and temperature gradients.
Astaxanthin Is Able to Prevent Alcohol-Induced Dysfunction of Liver Mitochondria

The search for new targets for the pathological action of ethanol remains an urgent task of biomedicine. Since degenerative changes in the liver are associated with the development of oxidative stress, antioxidants are promising agents for the treatment of alcohol-related diseases. In this work, we studied the ability of the carotenoid antioxidant, astaxanthin (AX), to prevent ethanol-induced changes in the liver of rats. It was shown that AX is able to protect the structure of mitochondria from degenerative changes caused by ethanol to improve mitochondrial functions. AX positively influences the activity and expression of proteins of the mitochondrial respiratory chain complexes and ATPase. In addition, a protective effect of AX on the rate and activity of mitochondrial respiration was demonstrated in the work. Thus, studies have shown that AX is involved in protective mechanisms in response to ethanol-induced mitochondrial dysfunction.
A Hierarchical Attention Model for Social Contextual Image Recommendation

Image based social networks are among the most popular social networking services in recent years. With a tremendous amount of images uploaded everyday, understanding users’ preferences on user-generated images and making recommendations have become an urgent need. In fact, many hybrid models have been proposed to fuse various kinds of side information (e.g., image visual representation, social network) and user-item historical behavior for enhancing recommendation performance. However, due to the unique characteristics of the user generated images in social image platforms, the previous studies failed to capture the complex aspects that influence users’ preferences in a unified framework. Moreover, most of these hybrid models relied on predefined weights in combining different kinds of information, which usually resulted in sub-optimal recommendation performance. To this end, in this paper, we develop a hierarchical attention model for social contextual image recommendation. In addition to basic latent user interest modeling in the popular matrix factorization based recommendation, we identify three key aspects (i.e., upload history, social influence, and owner admiration) that affect each user's latent preferences, where each aspect summarizes a contextual factor from the complex relationships between users and images. After that, we design a hierarchical attention network that naturally mirrors the hierarchical relationship (elements in each aspects level, and the aspect level) of users’ latent interests with the identified key aspects. Specifically, by taking embeddings from state-of-the-art deep learning models that are tailored for each kind of data, the hierarchical attention network could learn to attend differently to more or less content. Finally, extensive experimental results on real-world datasets clearly show the superiority of our proposed model.
Application of SERS in the Detection of Fungi, Bacteria and Viruses

In this review, we report the recent advances of SERS in fungi, bacteria, and viruses. Firstly, we briefly introduce the advantage of SERS over fluorescence on virus identification and detection. Secondly, we review the feasibility analysis of Raman/SERS spectrum analysis, identification, and fungal detection on SERS substrates of various nanostructures with a signal amplification mechanism. Thirdly, we focus on SERS spectra for nucleic acid, pathogens for the detection of viruses and bacteria, and furthermore introduce SERS-based microdevices, including SERS-based microfluidic devices, and three-dimensional nanostructured plasmonic substrates.
Elementary Numerical Computing With Mathematica

Applications computational errors algorithms error error propagation floating-point computation positional number systems floating-point numbers rounding basic operations numerical instability other difficulties rootfinding roots bisection method Newton's method functional interaction secant method systems of linear equations matrices norms and sensitivity Gaussian elimination accuracy of partial pivoting linear equation solvers large sparse systems LU factorization interpolation Taylor's series big-oh notation existence and uniqueness Lagrange form inverse interpolation least squares approximation the least squares problem orthogonality the normal equations numerical differentiation and integration numerical differentiation simple quadrature rates Gaussian quadrature rules numerical error estimates global adaptive quadrature ordinary differential equators a single ODE Euler's method systems of ODE's Taylor series methods Runge-Kutta methods overview. Appendices: Getting started with Mathematica exact and approximate numerical calculations algebraic calculations lists defining functions procedural programming expressions defining rules how evaluation works delayed evaluation.
Human Retrotransposons and Effective Computational Detection Methods for Next-Generation Sequencing Data

Transposable elements (TEs) are classified into two classes according to their mobilization mechanism. Compared to DNA transposons that move by the “cut and paste” mechanism, retrotransposons mobilize via the “copy and paste” method. They have been an essential research topic because some of the active elements, such as Long interspersed element 1 (LINE-1), Alu, and SVA elements, have contributed to the genetic diversity of primates beyond humans. In addition, they can cause genetic disorders by altering gene expression and generating structural variations (SVs). The development and rapid technological advances in next-generation sequencing (NGS) have led to new perspectives on detecting retrotransposon-mediated SVs, especially insertions. Moreover, various computational methods have been developed based on NGS data to precisely detect the insertions and deletions in the human genome. Therefore, this review discusses details about the recently studied and utilized NGS technologies and the effective computational approaches for discovering retrotransposons through it. The final part covers a diverse range of computational methods for detecting retrotransposon insertions with human NGS data. This review will give researchers insights into understanding the TEs and how to investigate them and find connections with research interests.
3D Scaffolds Fabrication via Bicomponent Microgels Assembly: Process Optimization and In Vitro Characterization

In the last decade, different technological approaches have been proposed for the fabrication of 3D models suitable to evaluate in vitro cell response. Among them, electro fluid dynamic atomization (EFDA) belonging to the family of electro-assisted technologies allows for the dropping of polysaccharides and/or proteins solutions to produce micro-scaled hydrogels or microgels with the peculiar features of hydrogel-like materials (i.e., biocompatibility, wettability, swelling). In this work, a method to fabricate 3D scaffolds by the assembly of bicomponent microgels made of sodium alginate and gelatin was proposed. As first step, optical and scanning electron microscopy with the support of image analysis enabled to explore the basic properties of single blocks in terms of correlation between particle morphology and process parameters (i.e., voltage, flow rate, electrode gap, and needle diameter). Chemical analysis via ninhydrin essays and FTIR analysis confirmed the presence of gelatin, mostly retained by physical interactions into the alginate network mediated by electrostatic forces. In vitro tests confirmed the effect of biochemical signals exerted by the protein on the biological response of hMSCs cultured onto the microgels surface. Hence, it is concluded that alginate/gelatin microgels assemblies can efficiently work as 3D scaffolds able to support in vitro cells functions, thus providing a friendly microenvironment to investigate in vitro cell interactions.
Optimal Portfolio under Five Constraints in the Markowitz Model and the Index Model

Investing in stocks is an inseparable part of modern life. Besides, choosing a good investment portfolio with high return and respectively low risk is the demand and desire of most modern investors. This paper tries to obtain an optimal investment portfolio to provide accurate and professional investment suggestions to the potential investors through the Markowitz Model and the Index Model under five different constraints. It has been considered several aspects to analyse the results: minimum variance portfolio, maximal Sharpe Ratio, etc. Some findings are given, including that the options of investment portfolio available under no constraint are larger compared to portfolios without permission of short-selling and investing SPX. Besides, the influence of the former is more significant than that of the latter; and then, the corresponding impact of different constraints ---is discussed through efficient frontiers. Additionally, comparing the Sharpe Ratio under two different models, it can be found that the Markowitz Model is a better option.
Recent Advances of Representative Optical Biosensors for Rapid and Sensitive Diagnostics of SARS-CoV-2

The outbreak of Corona Virus Disease 2019 (COVID-19) has again emphasized the significance of developing rapid and highly sensitive testing tools for quickly identifying infected patients. Although the current reverse transcription polymerase chain reaction (RT-PCR) diagnostic techniques can satisfy the required sensitivity and specificity, the inherent disadvantages with time-consuming, sophisticated equipment and professional operators limit its application scopes. Compared with traditional detection techniques, optical biosensors based on nanomaterials/nanostructures have received much interest in the detection of SARS-CoV-2 due to the high sensitivity, high accuracy, and fast response. In this review, the research progress on optical biosensors in SARS-CoV-2 diagnosis, including fluorescence biosensors, colorimetric biosensors, Surface Enhancement Raman Scattering (SERS) biosensors, and Surface Plasmon Resonance (SPR) biosensors, was comprehensively summarized. Further, promising strategies to improve optical biosensors are also explained. Optical biosensors can not only realize the rapid detection of SARS-CoV-2 but also be applied to judge the infectiousness of the virus and guide the choice of SARS-CoV-2 vaccines, showing enormous potential to become point-of-care detection tools for the timely control of the pandemic.
We the swarm—Methodological, theoretical, and societal (r)evolutions in collective decision-making research

Collective decision-making constitutes a core function of social systems and is, therefore, a central tenet of collective intelligence research. From fish schools to human crowds, we start by interrogating ourselves about the very definition of collective decision-making and the scope of the scientific research that falls under it. We then summarize its history through the lenses of social choice theory and swarm intelligence and their accelerating collaboration over the past 20 or so years. Finally, we offer our perspective on the future of collective decision-making research in 3 mutually inclusive directions. We argue (1) that the possibility to collect data of a new nature, including fine-grain tracking information, virtual reality, and brain imaging inputs, will enable a direct link between plastic individual cognitive processes and the ontogeny of collective behaviors; (2) that current theoretical frameworks are not well suited to describe the long-term consequences of individual plasticity on collective decision-making processes and that, therefore, new formalisms are necessary; and finally (3) that applying the results of collective decision-making research to real-world situations will require the development of practical tools, the implementation of monitoring processes that respect civil liberties, and, possibly, government regulations of social interventions by public and private actors.
IMPROVING THE FLUX CALIBRATION IN REVERBERATION MAPPING BY SPECTRAL FITTING:APPLICATION TO THE SEYFERT GALAXY MCG–6-30-15

We propose a method for the flux calibration of reverberation mapping spectra, based on accurate measurement of [O iii] λ5007 emission by spectral fitting. Our method can achieve better accuracy than the traditional method of Groningen & Wanders, allowing reverberation mapping measurements for objects with variability amplitudes as low as ∼5%. As a demonstration, we reanalyze the data of the Seyfert 1 galaxy MCG–6-30-15, taken from the 2008 campaign of the Lick active galactic nucleus monitoring project, which previously failed to obtain a time lag for this weakly variable object, owing to a relatively large flux calibration uncertainty. We detect a statistically significant rest-frame time lag of days between the Hβ and V-band light curves. Combining this lag with FWHM(Hβ) = 1933 ± 81 km s−1 and a virial coefficient of f = 0.7, we derive a virial black hole mass of M⊙, which agrees well with previous estimates by other methods.
Using boundary methods for estimating class separability

Designing and operating a classification system becomes drastically more difficult as the data dimensionality increases. A feature extraction (FE) step is often used to reduce the data dimensionality to mitigate this complexity. Thus FE may be viewed as a form of data compression whose objective is to minimize the consequences reducing the dimensionality has on class separability. This differs from the normal objective of data compression which is to minimize distortion, typically measured in the mean squared sense. It is often unclear whether the resulting features from a FE method provide an optimum set for classification. Further, extracting discrimination features from finite data sets increases in difficulty as the dimensionality of the data increases. The need for features to reduce complexity, combined with the difficulties of extracting features, justifies the need for studying ways of ranking feature sets for classification, i.e. feature set evaluation (FSE) techniques. This paper investigates Boundary Methods (BMs), a collection of tools used for distribution analysis, as a method for performing FSE. BMs provide a measure of class separability, the overlap sum (OS), which is strongly correlated with Bayes error and easily computed. These properties suggest BMs can be used as an alternative to traditional Bayes error estimation techniques. To demonstrate the utility of BMs as a FSE technique, this work examines the relationship between OS and Bayes error. This relationship is examined both analytically and empirically. The empirical results are obtained by running experiments which use both synthetic and measured data. Next, the computational complexity of using BMs as a FSE technique is explored through a comparison with the computational complexity of estimating Bayes error through the voting k-NN procedure. Finally, this work also examines the use of BMs as a novel method for estimating the modality of a data set.
Glucocorticoid-Regulated Kinase CAMKIγ in the Central Amygdala Controls Anxiety-like Behavior in Mice

The expression of the Calcium/Calmodulin-Dependent Protein Kinase I gamma (encoded by the Camk1g gene) depends on the activation of glucocorticoid receptors (GR) and is strongly regulated by stress. Since Camk1g is primarily expressed in neuronal cells of the limbic system in the brain, we hypothesized that it could be involved in signaling mechanisms that underlie the adaptive or maladaptive responses to stress. Here, we find that restraint-induced stress and the GR agonist dexamethasone robustly increase the expression of Camk1g in neurons of the amygdalar nuclei in the mouse brain. To assess the functional role of Camk1g expression, we performed a virally induced knock-down of the transcript. Mice with bilateral amygdala-specific Camk1g knock-down showed increased anxiety-like behaviors in the light-dark box, and an increase in freezing behavior after fear-conditioning, but normal spatial working memory during exploration of a Y-maze. Thus, we confirm that Camk1g is a neuron-specific GR-regulated transcript, and show that it is specifically involved in behaviors related to anxiety, as well as responses conditioned by aversive stimuli.
Prediction of Purchase Intention of High and Middle Potential Users in Luxury Hotels Based on Data Mining

In recent years, the term “big data” has attracted the attention of many scholars and business managers, and the emergence of massive data has ushered in a major transformation of the times. Service quality and customer repeat purchase intention are two of the hot issues in the field of service research. Clarifying the mechanism of the two can help enterprises establish long-term customer relations with more customers through the improvement of service quality, enhance their competitive strength, and improve enterprise performance. According to the actual consumer purchase data provided, the existing data sets are visually analyzed and processed to find the consumer purchase rules, build features and build a reasonable consumer purchase prediction experimental data set. Based on the obtained experimental data set, a single prediction model of consumer purchase is designed. According to the analysis of the instantiation results of the single prediction model, the single model consumed in the fusion model of consumer purchase prediction is determined. In order to demonstrate the viewpoints put forward, the research model is established, the research hypothesis is put forward, the questionnaire is designed by referring to the literature and field investigation, and the results of the questionnaire are empirically analyzed. Using the methods of descriptive statistical analysis, factor analysis, reliability and validity test, and regression analysis, the hypothesis that service quality has a direct impact on customer repeat purchase intention is verified.