Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel strategy to construct multi-strain starter cultures: an insight to evolve from natural to directed fermentation

Abstract Some biotechnological strategies have succeeded in the attempt to imitate natural fermentation, and bioprocesses have been efficiently designed when the product is the result of a unique biological reaction. However, when the process requires more than one biological reaction, few bioprocesses have been successfully designed because the available tools to construct multi-strain starter cultures are not yet well defined. In this work, a novel experimental strategy to construct multi-strain starter cultures with selected native microorganisms from natural fermentation is proposed. The strategy analyses, selects, and defines the number and proportion of each strain that should form a starter culture to be used in directed fermentations. It was applied to evolve natural fermentation to directed fermentation in distilled agave production. The results showed that a starter culture integrated by Kluyveromyces marxianus, Clavispora lusitaniae, and Kluyveromyces marxianus var. drosophilarum in proportions of 35, 32, and 33%, respectively, allows obtaining fermented agave juice containing a 2.1% alcohol yield and a distilled product with a broad profile of aromatic compounds. Hence, the results show, for the first time, a tool that addresses the technical challenge for multi-strain starter culture construction, offering the possibility of preserving the typicity and genuineness of the original traditional product.
Immunosuppressive reprogramming of neutrophils by lung mesenchymal cells promotes breast cancer metastasis

Neutrophils, the most abundant innate immune cells, function as crucial regulators of the adaptive immune system in diverse pathological conditions, including metastatic cancer. However, it remains largely unknown whether their immunomodulatory functions are intrinsic or acquired within the pathological tissue environment. Here, using mouse models of metastatic breast cancer in the lungs, we show that, although neutrophils isolated from bone marrow (BM) or blood are minimally immunosuppressive, lung-infiltrating neutrophils are robustly suppressive of both T cells and natural killer (NK) cells. We found that this tissue-specific immunosuppressive capacity of neutrophils exists in the steady state and is reinforced by tumor-associated inflammation. Acquisition of potent immunosuppression activity by lung-infiltrating neutrophils was endowed by the lung-resident stroma, specifically CD140a+ mesenchymal cells (MCs) and largely via prostaglandin-endoperoxide synthase 2 (PTGS2), the rate-limiting enzyme for prostaglandin E2 (PGE2) biosynthesis. MC-specific deletion of Ptgs2 or pharmacological inhibition of PGE2 receptors reversed lung neutrophil–mediated immunosuppression and mitigated lung metastasis of breast cancer in vivo. These lung stroma–targeting strategies substantially improved the therapeutic efficacy of adoptive T cell–based immunotherapy in treating metastatic disease in mice. Collectively, our results reveal that the immunoregulatory effects of neutrophils are induced by tissue-resident stroma and that targeting tissue-specific stromal factors represents an effective approach to boost tissue-resident immunity against metastatic disease. Description Lung-resident mesenchymal cells reprogram neutrophils to be immunosuppressive cells that facilitate lung metastasis of breast cancer. Protumoral neutrophils thrive in the lungs The lungs represent one of the most frequent sites for distant tumor metastasis, but the reason for their susceptibility to metastatic disease remains unclear. Using mouse models of lung-metastasizing breast cancer, Gong et al. found that the lung stroma endows infiltrating neutrophils with immunosuppressive features enabling strong suppression of antitumoral T and natural killer cells, an effect further strengthened by host inflammation. Immunosuppressive neutrophil reprogramming required production of the lipid mediator prostaglandin E2 (PGE2) by lung mesenchymal cells. Pharmacological blockade of the PGE2 receptors EP2 and EP4 reduced lung metastasis and enhanced the antimetastatic effects of adoptive T cell therapy. These results identify a key role for cross-talk between neutrophils and the lung stroma in tumor metastasis that may be disrupted by therapies blocking PGE2 signaling. —CO
Optical trapping forces on biological cells on a waveguide surface

A three dimensional finite element method is used to model the forces acting on red blood cells trapped on an optical waveguide surface. The parameters are chosen to correspond to strip waveguides made of tantalum pentoxide (Ta2O5). A wavelength of 1070 nm is used and the cells are taken to be spherical. Gradient and scattering forces experienced by the cells are studied and found to be highly dependent on the refractive index of the cells. Gradient forces are found to be one order of magnitude larger than scattering forces. Only the lower part of the cells is in contact with the evanescent field of the waveguide. For low refractive indices, we find that the lower 0.5-1 μm of the cells is sufficient to determine the optical forces. For the cell sizes considered, all forces increase with the size.
A numerical study on the existence of the Venturi-effect in passages between perpendicular buildings

The Venturi effect refers to the increase in fluid speed due to a decrease of the flow section in confined flows. The wind speed conditions in converging and diverging passages between perpendicular buildings are studied with computational fluid dynamics to investigate the extent to which the so-called Venturi effect is present in the passages. Model validation is performed by comparing the numerical results with wind tunnel measurements. The validated model is employed for a detailed investigation of the wind speed and the flow rate in the passages for a wide range of passage widths. The simulations show an increase in wind speed near ground level, but a decrease of horizontal wind speed in the upper part of the converging passages. The reason is the wind-blocking effect, which causes a large part of the oncoming wind to flow over and around the buildings, rather than being forced through the passage. Due to this effect, the flow rates through the converging passages are consistently lower than the free-field flow rate, implying that the term Venturi effect is less applicable for such building configurations.
Variational and Shape Prior-based Level Set Model for Image Segmentation

A new image segmentation model based on level sets approach is presented herein. We deal with radiographic medical images where boundaries are not salient, and objects of interest have the same gray level as other structures in the image. Thus, an a priori information about the shape we look for is integrated in the level set evolution for good segmentation results. The proposed model also accounts a penalization term that forces the level set to be close to a signed distance function (SDF), which then avoids the re-initialization procedure. In addition, a variant and complete Mumford-Shah model is used in our functional; the added Hausdorff measure helps to better handle zones where boundaries are occluded or not salient. Finally, a weighted area term is added to the functional to make the level set drive rapidly to object's boundaries. The segmentation model is formulated in a variational framework, which, thanks to calculus of variations, yields to partial differential equations (PDEs) to guide the level set evolution. Results obtained on both synthetic and digital radiographs reconstruction (DRR) show that the proposed model improves on existing prior and non-prior shape based image segmentation.
A Hotspot-Driven Semi-automated Competitive Analysis Framework for Identifying Compiler Key Optimizations

High-performance compilers play an important role in improving the run-time performance of a program, and it is hard and time-consuming to identify the key optimizations implemented in a high-performance compiler with traditional program analysis. In this paper, we propose a hotspot-driven semi-automated competitive analysis framework for identifying key optimizations through comparing the hotspot codes generated by any two different compilers. Our framework is platform-agnostic and works well on both AArch64 and X64 platforms, which automates the stages of hotspot detection and dynamic binary instrumentation only for selected hotspots. With the instrumented instruction characterization information, the framework users can analyze the binary code within a much smaller scope to explore practical optimizations implemented in any of the compilers compared. To demonstrate the effectiveness and practicality, we conduct experiments on SPECspeed 2017 Integer benchmarks(CINT2017) and their binaries generated by open-source GCC compiler versus proprietary Huawei BiSheng and Intel ICC compilers on AArch64 and X64 platforms respectively. Empirical studies show that our methods can identify several significant optimizations that have been implemented by proprietary compilers and as well can be implemented in open-source compilers. To Hangzhou Hongjun Microelectronics Technology(Hjmicro), the identified key optimizations shed great light on optimizing their GCC-based product compiler, which delivers 20.83% improvement for SPECrate 2017 Integer on AArch64 platform.
LAGrad: Statically Optimized Differentiable Programming in MLIR

Automatic differentiation (AD) is a central algorithm in deep learning and the emerging field of differentiable programming. However, the performance of AD remains a significant bottleneck in these fields. Training large models requires repeatedly evaluating gradients via AD potentially millions of times. Additionally, the most common form of AD incurs an asymptotically large memory cost relative to the original function being differentiated. This paper introduces LAGrad, a reverse-mode, source-to-source AD system that leverages high-level information in MLIR to produce efficient differentiated code. LAGrad employs a collection of novel static optimizations that benefit from the semantics of high-level MLIR dialects to exploit the sparsity and structured control flow of generated code. Using these, LAGrad is able to achieve speedups of up to 2.8× and use 35× less memory relative to state of the art AD systems on real-world machine learning and computer vision benchmarks.
Molecular Identification of and Damage Caused by Tribolium castaneum to Native Maize at Guerrero, Mexico

Abstract. In rural communities in the State of Guerrero, farmers keep seeds to plant during the dry season, and maize (Zea mays L.) and bean grains for 6 to 9 months as the main food for consumption at home. However, in recent years maize weevils and beetles have been found in containers where seeds and grains were stored. Maize from the containers was sampled to identify damage caused by insects. Maize weevil, Sitophilus zeamais (Motschulsky), and red flour beetle, Tribolium castaneum (Herbst), were detected in damaged maize kernels. According to molecular analysis, red flour beetle is another pest of maize used by farmers in rural communities in the State of Guerrero.
Resistive memory switching in layered oxides: AnBnO3n+2 perovskite derivatives and Bi2Sr2CaCu2O8+δ high‐Tc superconductor

Resistive memory switching was investigated in titanates and niobates of the type AnBnO3n+2 and in the high‐Tc superconductor Bi2Sr2CaCu2O8+δ. We studied the switching by current injection perpendicular to the layers. Both dc and pulsed measurements were performed. Out‐of‐plane transport properties were investigated by measurements of the resistance and current–voltage characteristics (IVs) vs. temperature for different resistive states. The critical temperature of superconducting transition and the critical current of intrinsic Josephson junctions were also analyzed for different resistive states in Bi2Sr2CaCu2O8+δ. The resistive memory switching was explained in terms of doping of the conducting layers, which is induced by trapped charges in the insulating layers. The charged insulating layers act as a floating gate and reduce or increase the carrier concentration in the conducting layers, respectively. We found that all studied materials demonstrate a different type of non‐persistent resistive switching at low temperatures. This type of switching shows up in a specific form of current–voltage characteristics with a pronounced back‐bending often called s‐shaped IV. Both types of resistive switching with and without memory effect were analyzed in terms of electron overheating. We examine the role of hot electrons and discuss additional factors, which might lead to persistent resistive states.
Fracture mechanical behaviour of visco‐elastic materials: application to the so‐called dwell‐effect

The material force approach is an efficient, elegant, and accepted means to compute the J‐integral as a fracture mechanical parameter for elastic and inelastic materials. With the formulation of a multiplicative split of the deformation gradient at hand, rate‐dependent (visco‐elastic) materials described for example by the physically based Bergström‐Boyce model can be investigated. For these investigations, the so‐called material volume forces have to be computed in order to separate the driving forces acting on the visco‐elastic zone around the crack tip from the driving forces acting on the crack tip itself, representing the crack driving force. To illustrate the effectiveness of this approach, the so‐called dwell‐effect of elastomeric materials is investigated.
Acoustic transients from planar axisymmetric vibrators using the impulse response approach

In the present paper an approach is presented to evaluate the transient near‐ and farfield pressures which result from the specified time‐dependent velocity of a finite axisymmetric vibrating planar surface. The surface may represent the vibrating face of a disk transducer or it may represent the vibrating surface of a membrane or plate. The approach is based on expanding the velocity of the vibrator as a summation of radially dependent functions with time‐dependent coefficients. The time‐dependent pressure at any point in the field is then expressed as a summation of convolution integrals which involve the time‐dependent coefficients and generalized impulse responses [P. Stepanishen, J. Acoust. Soc. Am. 49, 1628–1639 (1971)]. Applications of the approach to study the time‐dependent pressure field which results from the axisymmetric motion of a disk transducer, membrane, and plate are discussed and numerical results are presented for specific cases of interest. [This work was supported by NIH. ]
Thin Versus Thick Matching in the Mantel-Haenszel Procedure for Detecting DIF

This Monte Carlo study examined strategies for forming the matching variable for the Mantel-Haenszel (MH) differential item functioning (DIF) procedure; thin matching on total test score was compared to forms of thick matching, pooling levels of the matching variable. Data were generated using a three-parameter logistic (3PL) item response theory (IRT) model with common guessing parameter. Number of subjects and test length were manipulated, as were the difficulty, discrimination, and presence/absence of DIF in the studied item. Outcome measures were the transformed log-odds &Deltacirc; MH, its standard error, and the MH chi-square statistic. For short tests (5 or 10 items), thin matching yielded very poor results, with a tendency to falsely identify items as possessing DIF against the reference group. The best methods of thick matching yielded outcome measure values closer to the expected value for non-DIF items, as well as a larger value than thin matching when the studied item possessed DIF. Intermediate length tests yielded similar results for thin matching and the best methods of thick matching. The method of thick matching that performed best depended on the measure used to detect DIF. Both difficulty and discrimination of the studied item were found to have a strong effect on the value of &Deltacirc; MH.
Start-to-end simulation of TAC SASE FEL facility

We study start-to-end simulation of the proposed Turkish Accelerator Center (TAC) Self Amplified Spontaneous Emission (SASE) Free Electron Laser (FEL) Facility. Astra Code is used to simulate the electron gun, including space charge effects and Elegant Code is used to track particle distribution from the accelerator modules through the entrance of the undulator including weak fields but not including space charge effects. Undulator optimization is performed using Genesis 1.3. In this study, tentative electron beam and laser parameters of the SASE FEL Facility are given.
Bias and Efficiency of Meta-Analytic Variance Estimators in the Random-Effects Model

The meta-analytic random effects model assumes that the variability in effect size estimates drawn from a set of studies can be decomposed into two parts: heterogeneity due to random population effects and sampling variance. In this context, the usual goal is to estimate the central tendency and the amount of heterogeneity in the population effect sizes. The amount of heterogeneity in a set of effect sizes has implications regarding the interpretation of the meta-analytic findings and often serves as an indicator for the presence of potential moderator variables. Five population heterogeneity estimators were compared in this article analytically and via Monte Carlo simulations with respect to their bias and efficiency.
A Novel Flying Magnetic Readout Head Based on Magneto-Optic Transfer

We have developed a novel flying magnetic readout Lead based on the concept of magneto-optical transfer. A special feature of this head is that its slider is made of a GGG substrate itself. To improve the readout characteristic of this head, the optical system was adjusted and the grooves were formed on a magnetic garnet film. The readout waveform and signal envelope became stable and the maximum CNR obtained was 52 dB (RBW=30 kHz). The CNR was almost constant for the recorded track width from 80 /spl mu/m to 2.7 /spl mu/m.
Face recognition under variable lighting using local qualitative representations

In this paper, a face recognition method using local qualitative representations is proposed to solve the problem of face recognition in varying lighting. Based on the observation that the ordinal relationship between the average brightness of image regions pair is invariant under lighting changes, Local Binary Mapping is defined as an illumination invariant for face recognition based on Local Binary Pattern descriptor, which extracts the local variance features of an image. For the 'symbol' feature vector, hamming distance is used as similarity measurement. It has been proved that the proposed method can provide the accuracy of 100 percent for subset 2, 3, 4 and 98.89 percent for subset 5 of the Yale facial database B when all images in subset 1 are used as gallery.
Generalized camera calibration model for trapezoidal patterns on the road

Abstract. We introduce a generalized camera calibration model that is able to determine the camera parameters without requiring perfect rectangular road-lane markings, thus overcoming the limitations of state-of-the-art calibration models. The advantage of the new model is that it can cope in situations where road-lane markings do not form a perfect rectangle, making calibration by trapezoidal patterns or parallelograms possible. The model requires only four reference points—the lane width and the length of the left and right lane markings—to determine the camera parameters. Through real-world surveying experiments, the new model is shown to be effective in defining the 2D/3D transformation (or vice versa) when there is no rectangular pattern on the road, and can also cope with trapezoidal patterns, near-parallelograms, and imperfect rectangles. This development greatly increases the flexibility and generality of traditional camera calibration models.
Experimental study of wave propagation through a rapidly created plasma

Frequency upconversion and converting a CW source microwave into a frequency upshifted and chirped periodic pulse have been demonstrated by two experiments. In the first one, the CW source microwave propagates through a periodically microwave-discharged plasma. The CW source microwave is converted into a periodic pulse having upshifted carrier frequency. The second one uses a high-voltage ( approximately 100-kV) DC discharge to generate a dense plasma suddenly between two parallel plates. A frequency upshifted and chirped pulse ( approximately 2 ns) converting from the CW source microwave interacting with the suddenly created plasma is observed. The central frequency ( approximately 6.4 GHz) of the pulse is upshifted from the frequency ( approximately 4.7 GHz) of the source wave by about 40%. Moreover, frequency components which are upshifted as high as 80% are also observed. >
Two-slit experiment: quantum and classical probabilities

Inter-relation between quantum and classical probability models is one of the most fundamental problems of quantum foundations. Nowadays this problem also plays an important role in quantum technologies, in quantum cryptography and the theory of quantum random generators. In this letter, we compare the viewpoint of Richard Feynman that the behavior of quantum particles cannot be described by classical probability theory with the viewpoint that quantum–classical inter-relation is more complicated (cf, in particular, with the tomographic model of quantum mechanics developed in detail by Vladimir Man'ko). As a basic example, we consider the two-slit experiment, which played a crucial role in quantum foundational debates at the beginning of quantum mechanics (QM). In particular, its analysis led Niels Bohr to the formulation of the principle of complementarity. First, we demonstrate that in complete accordance with Feynman's viewpoint, the probabilities for the two-slit experiment have the non-Kolmogorovian structure, since they violate one of basic laws of classical probability theory, the law of total probability (the heart of the Bayesian analysis). However, then we show that these probabilities can be embedded in a natural way into the classical (Kolmogorov, 1933) probability model. To do this, one has to take into account the randomness of selection of different experimental contexts, the joint consideration of which led Feynman to a conclusion about the non-classicality of quantum probability. We compare this embedding of non-Kolmogorovian quantum probabilities into the Kolmogorov model with well-known embeddings of non-Euclidean geometries into Euclidean space (e.g., the Poincaré disk model for the Lobachvesky plane).
The Art of Probability-of-Default Curve Calibration

PD curve calibration refers to the transformation of a set of rating grade level probabilities of default (PDs) to another average PD level that is determined by a change of the underlying portfolio-wide PD. This paper presents a framework that allows to explore a variety of calibration approaches and the conditions under which they are fit for purpose. We test the approaches discussed by applying them to publicly available datasets of agency rating and default statistics that can be considered typical for the scope of application of the approaches. We show that the popular 'scaled PDs' approach is theoretically questionable and identify an alternative calibration approach ('scaled likelihood ratio') that is both theoretically sound and performs better on the test datasets. Keywords: Probability of default, calibration, likelihood ratio, Bayes' formula, rating profile, binary classification.