Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual oxidase 1 is dispensable during Mycobacterium tuberculosis infection in mice

Introduction Mycobacterium tuberculosis (Mtb) is the primary cause of human tuberculosis (TB) and is currently the second most common cause of death due to a singleinfectious agent. The first line of defense against airborne pathogens, including Mtb, is the respiratory epithelium. One of the innate defenses used by respiratory epithelial cells to prevent microbial infection is an oxidative antimicrobial system consisting of the proteins, lactoperoxidase (LPO) and Dual oxidase 1 (Duox1), the thiocyanate anion (SCN-) and hydrogen peroxide (H2O2), which together lead to the generation of antimicrobial hypothiocyanite (OSCN-) in the airway lumen. OSCN- kills bacteria and viruses in vitro, but the role of this Duox1-based system in bacterial infections in vivo remains largely unknown. The goal of this study was to assess whether Duox1 contributes to the immune response against the unique respiratory pathogen, Mtb. Methods Duox1-deficient (Duox1 KO) and wild-type (WT) mice were infected with Mtb aerosols and bacterial titers, lung pathology, cytokines and immune cell recruitment were assessed. Results and discussion Mtb titers in the lung, spleen and liver were not different 30 days after infection between WT and Duox1 KO mice. Duox1 did not affect lung histology assessed at days 0, 30, and 90 post-Mtb infection. Mtb-infected Duox1 KO animals exhibited enhanced production of certain cytokines and chemokines in the airway; however, this response was not associated with significantly higher numbers of macrophages or neutrophils in the lung. B cell numbers were lower, while apoptosis was higher in the pulmonary lesions of Mtb-infected Duox1 KO mice compared to infected WT animals. Taken together, these data demonstrate that while Duox1 might influence leukocyte recruitment to inflammatory cell aggregates, Duox1 is dispensable for the overall clinical course of Mtb lung infection in a mouse model.
X-ray imaging performance of the flight model JET-X telescope

Construction of the flight model joint European X-ray telescope (JET-X) for the Russian spectrum-X mission has been completed and performance tests and calibration of the instrument have been carried out. Separate measurements of the responses of the x-ray mirrors, the CCD detectors and the optical filters already indicate that JET-X will achieve spatial resolutions of around 20 arcsec, an on-axis collecting area of 310 cm2 at 1.5 keV and an energy resolution of 130 eV at 6 keV. As a final step in the calibration of the telescope assembly, end-to-end x-ray tests on the complete instrument have been performed in the x-ray beam line facility at MPE Garching. Results from this calibration program are reported and the overall response of the two x-ray telescopes are compared with the previously measured responses of the mirror, the CCD detectors and the optical filters. In-orbit sensitivity responses are derived from these calibration data sets, for the normal operating modes of JET-X.
Vibration Control of a Cylindrical Shell Structure Using Macro Fiber Composite Actuators

We studied the vibration suppression of an end-capped cylindrical shell structure with surface bonded macro fiber composite actuators. The dynamic characteristics of the cylindrical shell structure were first analyzed, and then a negative velocity feedback algorithm was applied to suppress the structural vibration at resonance and nonresonance vibration frequencies. The modal mass and stiffness matrix of the smart cylindrical shell structure were extracted for the controller design. An active controller was designed to suppress vibration of the smart structure, and the control performance was evaluated in resonance and nonresonance regimes. It was found that structural vibration was reduced by adopting a proper negative velocity feedback control algorithm in both resonance and nonresonance regimes.
PARA-BLASCHKE ISOPARAMETRIC HYPERSURFACES IN A UNIT SPHERE Sn + 1(1)*

Abstract Let A = ρ2∑i,jAijθi ⊗ θj and B = ρ2∑i,jBij θi ⊗ θj be the Blaschke tensor and the Möbius second fundamental form of the immersion x. Let D = A + λB be the para-Blaschke tensor of x, where λ is a constant. If x: Mn ↦ Sn + 1(1) is an n-dimensional para-Blaschke isoparametric hypersurface in a unit sphere Sn + 1(1) and x has three distinct Blaschke eigenvalues one of which is simple or has three distinct Möbius principal curvatures one of which is simple, we obtain the full classification theorems of the hypersurface.
Dynamics of Boolean Networks

Networks are used to model systems consisting of many interacting units. The topology of networks has been studied extensively while there are still many open questions concerning the dynamics of and on networks. Boolean networks refer to a class of dynamics on networks; it is the simplest possible dynamics which allows for analytical studies and easy computer implementation. Applications of networks in general and Boolean networks in particular can be found in numerous fields, ranging from chemistry, biology, economy, computer sciences, linguistics, to sociology and geology. A classical Boolean network was introduced by Stuart Kauffman as a simple model for gene regulation. The Boolean state of a node is determined by a Boolean function whose arguments are the states of its randomly chosen inputs. The inputs are those nodes from which a edge of the network leads to the node under consideration. Key quantities for the dynamics are the number and size of attractors. An attractor is a recurrent set of a network’s states. Although the classical model of random Boolean networks with synchronous updating has been introduced in 1969 it took 30 years for an analytical understanding of its key quantities. The present work studies variations of the classical implementation of a random Boolean network, in particular models with differently defined dynamics (non-synchronous updating, the ensemble of threshold functions instead of all Boolean functions) and systems with other connection patterns (scale-free in-degree distribution). To study the characteristics of the dynamics of those variations both, computer simulations and analytic methods, are used. For the classical critical Boolean network (with synchronous updating) it is known that both, the mean number and the length of attractors, diverge faster than any power law with the number of nodes. It is shown how this changes for asynchronous updating schemes, in particular for a deterministic asynchronous one. Boolean threshold functions lead new phases of the dynamics which are not predicted by the usual mean-field considerations, real genetic networks might therefore also have a richer dynamical behaviour than the classical dynamical phases. For the scale-free topology, the number of the non-frozen nodes scales in a different way as in networks with a fixed number of inputs. Here, it is possible to analytically explain numerical findings in literature.
Cylindrical wave diffraction by a perfectly conducting strip in a homogeneous bi-isotropic medium

Abstract The diffraction of an electromagnetic wave by a perfectly conducting strip in a bi-isotropic medium is studied. The problem is solved using integral transforms, the Wiener-Hopf technique and asymptotic methods. The diffracted field is found to be the sum of the fields produced by the two edges of the strip and an interaction field.
The Hippo signaling pathway: from multiple signals to the hallmarks of cancers

Evolutionarily conserved, the Hippo signaling pathway is critical in regulating organ size and tissue homeostasis. The activity of this pathway is tightly regulated under normal circumstances, since its physical function is precisely maintained to control the rate of cell proliferation. Failure of maintenance leads to a variety of tumors. Our understanding of the mechanism of Hippo dysregulation and tumorigenesis is becoming increasingly precise, relying on the emergence of upstream inhibitor or activator and the connection linking Hippo target genes, mutations, and related signaling pathways with phenotypes. In this review, we summarize recent reports on the signaling network of the Hippo pathway in tumorigenesis and progression by exploring its critical mechanisms in cancer biology and potential targeting in cancer therapy.
Method of Optimization for Target Localization Model Parameters Based on LSSVR

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.
Phytochemical Profile of Trigonella caerulea (Blue Fenugreek) Herb and Quantification of Aroma-Determining Constituents

The herb of Trigonella caerulea (Fabaceae), commonly known as blue fenugreek, is used for the production of traditional cheese and bread varieties in the Alpine region. Despite its frequent consumption, only one study so far has focused on the constituent pattern of blue fenugreek, revealing qualitative information on some flavor-determining constituents. However, with regard to the volatile constituents present in the herb, the applied methods were insufficient and did not take relevant terpenoids into account. In the present study, we analyzed the phytochemical composition of T. caerulea herb applying a set of analytical methods, such as headspace-GC, GC-MS, LC-MS, and NMR spectroscopy. We thus determined the most dominant primary and specialized metabolites and assessed the fatty acid profile as well as the amounts of taste-relevant α-keto acids. In addition, eleven volatiles were quantified, of which tiglic aldehyde, phenylacetaldehyde, methyl benzoate, n-hexanal, and trans-menthone were identified as most significantly contributing to the aroma of blue fenugreek. Moreover, pinitol was found accumulated in the herb, whereas preparative works led to the isolation of six flavonol glycosides. Hence, our study shows a detailed analysis of the phytochemical profile of blue fenugreek and provides an explanation for its characteristic aroma and its health-beneficial effects.
Tissue-engineered edible bird’s nests (TeeBN)

Edible bird’s nests (EBN)—the nests of swiftlet birds harvested from the wild— are high-end healthcare food in East Asia, while their excessive harvesting poses increasing ecological, environmental, and food safety concerns. Here, we report for the first time a tissue-engineering (TE) approach for fabricating EBNs substitutes by integrating the technologies of three-dimensional (3D) printing and live cell culture. The engineered products, tissue-engineered edible bird’s nests (TeeBN), comprise two layers. The first is a feeding layer that encapsulates epithelial cells in 3D-printed biocompatible gelation scaffolds. These cells secrete bioactive ingredients, e.g., sialic acid and epidermal growth factors (EGF), recapitulating the natural production of these substances by birds. The second is a receiving layer, consisting of foodgrade natural polymers, e.g., polysaccharides, which mimics the building blocks of natural EBNs while biologically stabilizing the factors released from the feeding layer. In vitro characterizations demonstrate that the feeding layer facilitates 3D cell growth and functions, and the receiving layer (as the end product) contains the necessary nutrients expected from natural EBNs—while without harmful substances commonly detected in natural EBNs. Further, in vivo metabolomics studies in mice indicate that TeeBN showed a similar profile of serum metabolites as natural EBN, reflecting comparable nutritional effects. In summary, we innovatively developed a tissue engineering-based substitute for EBNs with comparable metabolic functions and minimized safety risks, opening a new avenue for producing delicacy food from laboratorial cell culture with 3D printing technology.
A room temperature, or moderately cooled, fast THz semiconductor hot electron bolometer

A fast THz bolometer is proposed in which, unlike the conventional thermal one, electromagnetic radiation heats only electrons in a narrow gap semiconductor without its lattice inertial heating. Under determined conditions, this heating changes generation–recombination processes that cause the carrier number to decrease and the semiconductor resistance to rise. The Hg0.8Cd0. 2Te detector noise equivalent power in the range of 77–300 K can reach ∼10−11 W for frequencies of about 1 THz and signal gain frequency bandwidth of 1 Hz. Measurements with the device prototype confirmed the concept of the proposed bolometer creation.
TESTING NON-NESTED NONLINEAR REGRESSION MODELS

In Pesaran [9], the test developed by Cox for comparing separate families of hypotheses was applied to the choice between two non-nested linear single-equation econometric models. In this paper, the analysis is extended to cover multivariate nonlinear models whenever full information maximum likelihood estimation is possible. This allows formal comparisons not only of competing explanatory variables but also of alternative functional forms. The largest part of the paper derives the results and shows that they are recognizable as generalizations of the single-equation case. It is also shown that the calculation of the test statistic involves very little computation beyond that necessary to estimate the models in the first place. The paper concludes with a practical application of the test to the analysis of the U.S. consumption function and it is demonstrated that formal tests can give quite different results to conventional informal selection procedures. Indeed, in the case examined, five alternative hypotheses, some of which appear to perform quite satisfactorily, can all be rejected using the test. THE NEED FOR STATISTICAL PROCEDURES for testing separate families of hypotheses has become more acute with the increased use of econometric techniques in practice. The usual F tests can only be applied to test nested hypotheses, i.e. those which are members of the same family. However, in practice, one is frequently faced with the problem of testing non-nested hypotheses. In an earlier article, Pesaran [9] applied the test developed by Cox [3, 4], for separate families of hypotheses to single-equation linear regression models both with autocorrelated and nonautocorrelated disturbances. In that paper, the question was confined to the selection of appropriate explanators for a given dependent variable. However, in much applied work, the investigator is required not merely to select variables but simultaneously to find an appropriate functional form. This problem can be especially acute since in many areas of research, economic theory can guide us in the choice of variables, but helps very little in the choice of functional form. As computing capacity has increased, and nonlinear estimation has become routine, the use of linearity has become more a matter of choice than of necessity; the criteria for such a choice are thus of considerable practical importance. In this paper, we extend the earlier analysis to cover these problems by deriving the comparable statistics without assuming linearity of the models. This allows formal comparisons of different explanatory variables, of different functional forms, and of the interactions between the two. We also extend the results to cover competing systems of nonlinear equations whenever full-information maximum-likelihood estimation is possible. This allows the test to be applied to non-nested simultaneous equation models as well
Comparison between self‐organizing map and principal component analysis for water quality assessment and hydro‐geochemical characterization in dyke intruded complex geological settings

Hydro‐geochemical characterization is challenging in dyke intruded complex geological setting. The comparison between self‐organizing map (SOM) classification and principal component analysis (PCA) is used for better understanding of hydrogeological process surrounding Amarpur dyke in Dhanbad district, Jharkhand. Total 30 water samples were collected and tested for 12 physicochemical parameters. The K‐means clustering with SOM grouped the water quality data into cluster 1 (46.67%, low mineralization), cluster 2 (36.67%, moderate mineralization) and cluster 3 (16.67%, high mineralization). The clusters of the majority of samples identified by PCA analysis is almost same as identified by SOM with little difficulty in discriminating between cluster 2 and cluster 3. The transformation of Ca‐HCO3 to Ca‐Cl‐SO4 occurred because of exchange of Ca2+ with Na+ adsorbed in the aquifer leading excess of sulphate ions. The results of this study suggest that SOM is an effective tool for a better understanding of patterns and processes driving water quality.
Deep Learning-Based Multiscale Pyramid Sieve and Analysis Module in Image Segmentation

Image segmentation is an essential part of remote sensing preprocessing. This paper proposes a deep learning-based multiscale pyramid sieve and analysis module to solve the problems associated with multiscale object coexistence in the complex semantic environments of multispectral remote sensing images. The image information distribution features, which change with scales in different convolutional feature map pyramids, were investigated, and the relationship between informative scales and the variation function curve was examined. Then, the proposed sieve and analysis module was designed according to the results. The proposed module is described as follows. (1) First, a feature map pyramid is built via a convolution calculation. (2) Sieve informative feature layers from the feature map pyramid with variation analysis. (3) Fuse informative feature layers. (4) The final feature representation is input into subsequent calculations for image segmentation. Experimental results demonstrate that, compared to the control group, the precision of the experimental group improved by 3.57%-5.89%, and the intraclass conformity improved by 2.74%-5.58%. In addition, the intraclass chaos decreased by 1.5%-7.9%. The experimental results demonstrate that the proposed multiscale pyramid sieve and analysis module can separate informative feature layers and improve the hierarchical segmentation accuracy of remote sensing multispectral images.
On enantiomorph‐polarity estimation

The behaviour of Rogers's η parameter for enantiomorph-polarity estimation is examined theoretically and experimentally on simulated intensity data for seven well-assorted compounds. An alternative parameter x, based on incoherent scattering from twin components related by a centre of symmetry, is also considered. It is found that both parameters are very well adapted to implementation in a least-squares program and converge well. The η parameter can give false and over-precise indications of chirality-polarity for structures which are nearly centrosymmetric, whereas the x parameter does not have this fault and converges more rapidly than η.
World modeling in robotics

Domestic robots are typically confronted with a complex, dynamically changing and unstructured environment. In such environments, robots must be able to perform a wide variety of tasks, e.g., safe navigation, object manipulation, including many different objects. An important prerequisite for successfully accomplishing these tasks is the availability of an accurate description of the environment the robot is operating in. In this work, such a description of the environment will be referred to as world model. A world model usually contains 3D-positions of uniquely labeled objects. In dynamic scenarios, dynamic object attributes such as velocities are included as well [1] and depending on the tasks any other object attribute can be added. The robot perceives the world using a perceptual system possibly containing multiple sensors such as cameras and laser scanners. The perceptual system generates measurements, i.e., time-stamped features, that can be anything from colored blobs in a camera image [2] to labeled object positions [1]. These measurements are the input for the algorithm that constructs the world model.
Asynchronous stochastic learning control with accelerative factor in multivariable systems

The asynchronous stochastic learning control system (ASLC) proposed by Zengqi Sun and Zhidong Deng (1993), which is able to cope with unrepetitiveness of SISO systems with measurement noise, is extended to multivariable control systems in this paper. By using stochastic approximation algorithms, an asynchronous stochastic learning control law is derived and corresponding convergence proofs are strictly given. To speed up the convergence of stochastic learning. The ASLC with accelerative factor is presented. A simulation example is given.
Calculation of the energy loss in giant magnetic impedance elements using the complex magnetic permeability spectra

Abstract The giant magnetic impedance (GMI) effect in ferromagnetic materials has been investigated for sensing applications. The GMI properties were evaluated via numerical solution of the complex magnetic permeability of the material. MATLAB® simulation was carried out to study the frequency dependence of magnetic permeability via obtaining solutions of the Landau–Lifshitz–Gilbert (LLG) and the Maxwell’s equations. The results indicate that the complex magnetic permeability peaks at a frequency of 6 GHz, corresponding to the ferromagnetic resonant (FMR) frequency, where the energy loss is maximum. A variation of the Gilbert damping parameter ( α ) associated with the LLG equation inversely affects this peak value. The area under the curve of complex magnetic permeability, calculated through counting the number of pixels within the image, provides an estimate of the average energy loss density within the material and appears to be consistent with the variation of the peak intensity.