Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization, High-Density Fermentation, and the Production of a Directed Vat Set Starter of Lactobacilli Used in the Food Industry: A Review

Lactobacilli have been widely concerned for decades. Bacteria of the genus Lactobacillus have been commonly employed in fermented food to improve the appearance, smell, and taste of food or prolong its shelf-life. They comprise 261 species (by March 2020) that are highly diverse at the phenotypic, ecological, and genotypic levels. Some Lactobacilli strains have been documented to be essential probiotics, which are defined as a group of living microorganisms that are beneficial to the health of the host when ingested in sufficiency. However, the characterization, high-density fermentation, and the production of a directed vat set (DVS) starter of Lactobacilli strains used in the food industry have not been systematically reported. This paper mainly focuses on reviewing Lactobacilli as functional starter cultures in the food industry, including different molecular techniques for identification at the species and strain levels, methods for evaluating Lactobacilli properties, enhancing their performance and improving the cell density of Lactobacilli, and the production techniques of DVS starter of Lactobacilli strains. Moreover, this review further discussed the existing problems and future development prospects of Lactobacilli in the food industry. The viability and stability of Lactobacilli in the food industry and gastrointestinal environment are critical challenges at the industrial scale. The new production equipment and technology of DVS starter of Lactobacilli strains will have the potential for large-scale application, for example, developing low-temperature spray drying, freezing granulation drying, and spray freeze-drying.
Nerve Density and Neuronal Biomarkers in Cancer

Simple Summary Researchers have shown that tumor biomarkers and increased nerve density are important clinical tools for determining cancer prognosis and developing effective treatments. The aims of our review were to synthesize these findings by detailing the histology of peripheral nerves, discuss the use of various neuronal biomarkers in cancer, and assess the impact of increased nerve density on tumorigenesis. This review demonstrates that specific neuronal markers may have an important role in tumorigenesis and may serve as diagnostic and prognostic factors for various cancers. Moreover, increased nerve density may be associated with worse prognosis in different cancers, and cancer therapies that decrease nerve density may offer benefit to patients. Abstract Certain histologic characteristics of neurons, novel neuronal biomarkers, and nerve density are emerging as important diagnostic and prognostic tools in several cancers. The tumor microenvironment has long been known to promote tumor development via promoting angiogenesis and cellular proliferation, but new evidence has shown that neural proliferation and invasion in the tumor microenvironment may also enable tumor growth. Specific neuronal components in peripheral nerves and their localization in certain tumor sites have been identified and associated with tumor aggressiveness. In addition, dense neural innervation has been shown to promote tumorigenesis. In this review, we will summarize the histological components of a nerve, explore the neuronal biomarkers found in tumor sites, and discuss clinical correlates between tumor neurobiology and patient prognosis.
Galaxy formation on the largest scales: the impact of astrophysics on the baryonic acoustic oscillation peak

We investigate the effects of galaxy formation on the baryonic acoustic oscillations (BAO) peak by applying semi-analytic modelling techniques to the Millennium-XXL, a 3×10 11 particle N-body simulation of similar volume to the future EUCLID survey. Our approach explicitly incorporates the effects of tidal fields and stochasticity on halo formation, as well as the presence of velocity bias, spatially correlated merger histories, and the connection of all these with the observable and physical properties of galaxies. We measure significant deviations in the shape of the BAO peak from the expectations of a linear bias model built on top of the nonlinear dark matter distribution. We find that the galaxy correlation function shows an excess close to the maximum of the BAO peak (r � 110h 1 Mpc) and a deficit at r � 90h 1 Mpc. Depending on the redshift, selection criteria and number density of the galaxy samples, these bias distortions can be up to 5% in amplitude. They are, however, largely absorbed by marginalization over nuisance parameters in current analytical modelling of the BAO peak in configuration space, in particular into the parameter that controls the broadening due to nonlinear evolution. As a result, the galaxy formation effects detected here are unlikely to bias the high-precision measurements planned by the upcoming generation of wide-field galaxy surveys.
Research on Student Performance Prediction Based on Stacking Fusion Model

Online learning is gradually becoming popular with the continuous development of Internet technology and the rapid development of educational informatization. It plays a key role in predicting students’ course performance based on their online learning behavior. It can optimize the effects of teaching and improve teaching strategies. Student performance prediction models that are built with a single algorithm currently have limited prediction accuracy. Meanwhile, model fusion improvement technology can combine many algorithms into a single model, thereby enhancing the overall effect of the model and providing better performance. In this paper, a stacking fusion model based on RF-CART–XGBoost–LightGBM is proposed. The first layer of the model uses a decision tree (CART), random forest, XGBoost and LightGBM as the base models. The second layer uses the LightGBM model. We used the Kalboard360 student achievement dataset, and features related to online learning behavior were selected as the model’s input for model training. Finally, we employed five-fold cross-validation to assess the model’s performance. In comparison with the four single models, the two fusion models based on the four single models both show significantly better performance. The prediction accuracies of the bagging fusion model and stacking fusion model are 83% and 84%, respectively. This proves that the proposed stacking fusion model has better performance, which helps to improve the accuracy of the performance prediction model further. It also provides an effective basis for optimizing the effects of teaching.
Ultra-Hypofractionated Stereotactic Body Radiotherapy for Localized Prostate Cancer: Clinical Outcomes, Patterns of Recurrence, Feasibility of Definitive Salvage Treatment, and Competing Oncological Risk

A cohort of 650 patients treated for localized prostate cancer (PCa) with CyberKnifeTM ultra-hypofractionated radiotherapy between 2011 and 2018 was retrospectively analyzed in terms of survival, patterns of failure, and outcomes of second-line definitive salvage therapies. The analysis was performed using survival analysis including the Kaplan–Meier method and Cox regression analysis. At a median follow-up of 49.4 months, the main pattern of failure was local–regional failure (7.4% in low-, and 13% in intermediate/high-risk group at five years), followed by distant metastases (3.6% in low-, and 6% in intermediate/high-risk group at five years). Five-year likelihood of developing a second malignancy was 7.3%; however, in the vast majority of the cases, the association with prior irradiation was unlikely. The 5-year overall survival was 90.2% in low-, and 88.8% in intermediate/high-risk patients. The independent prognostic factors for survival included age (HR 1.1; 95% CI 1.07–1.14) and occurrence of a second malignancy (HR 3.67; 95% CI 2.19–6.15). Definitive local salvage therapies were feasible in the majority of the patients with local–regional failure, and uncommonly in patients with distant metastases, with an estimated second-line progression free survival of 67.8% at two years. Competing oncological risks and age were significantly more important for patients’ survival compared to primary disease recurrence.
A Walk through Gumboro Disease

Infectious bursal disease (IBD), caused by an Avibirnavirus, belonging to the family Birnaviridae, is an immunosuppressive disease that affects 3–6-week-old chickens, resulting in clinical or subclinical infection. Although clinical disease occurs in chickens, turkeys, ducks, guinea fowl, and ostriches can be also infected. IBD virus (IBDV) causes lymphoid depletion of the bursa, which is responsible for the severe depression of the humoral antibody response, primarily if this occurs within the first 2 weeks of life. IBD remains an issue in chicken meat production due to economic losses caused by the spread of variants or subtypes, resistant to the most common vaccines, responsible for a subclinical disease characterized by reduced growth performance and increased susceptibility to secondary infections. Very virulent strains of classical serotype 1 are also common in several countries and can cause severe disease with up to 90% mortality. This review mainly focuses on the immunosuppressive effect of the IBDV and potential vaccination strategies, capable of overcoming challenges associated with the optimal time for vaccination of offspring, which is dependent on maternal immunity and IBDV variant occurrence.
Near infrared spectroscopy of M dwarfs. III. Carbon and oxygen abundances in late M dwarfs including the dusty rapid rotator 2MASSI J1835379+325954

Carbon and oxygen abundances of eight late M dwarfs are determined based on the near IR spectra of medium resolution. Seven objects with T_eff above 2600K are analyzed with the dust-free models. The M8.5 dwarf 2MASSI J1835379+325954 whose T_eff is 2275K is analyzed by the dusty model, in which the surface temperature is higher by about 600K due to the blanketing effect of the dust, and C and O abundances are higher by 0.25 and 0.15dex, respectively, compared to the analysis by the dust-free model. Once dust forms in the photosphere, the dust works as a kind of thermostat and temperatures of the surface layers remain nearly the same as the condensation temperatures of the dust grains. For this reason, the temperatures of the surface layers of the dusty dwarfs are not sensitive to the fundamental parameters including T_eff. Also, 2MASS J1835379 +325954 is a rapid rotator, for which its EWs are thought to remain unchanged by the rotational broadening. This is, however, true only when the true continuum is well defined. Otherwise, the pseudo-continuum level depends on the rotational velocity and hence the EWs as well. For this reason, the derived abundances depend on the rotational velocity assumed: For the values of V_rot*sin(i)=37.6 and 44.0km/s available in the literature, the derived C and O abundances differ by 0.23 and 0.14dex, respectively, and we find that the higher value provides a better account of the observed spectrum. The resulting C and O abundances in our late M dwarfs show no systematic difference from our results for the early and middle M dwarfs, and confirm the higher O/C ratio at the lower metallicity. In late M dwarfs, CO and H2O remain as excellent abundance indicators of C and O, respectively, except for additional uncertainty due to the complexity associated with the dust formation in the latest M dwarfs.
ALMA OBSERVATIONS OF INFALLING FLOWS TOWARD THE KEPLERIAN DISK AROUND THE CLASS I PROTOSTAR L1489 IRS

We have conducted ALMA observations in the 1.3 mm continuum and 12CO (2–1), C18O (2–1), and SO (56–45) lines toward L1489 IRS, a Class I protostar surrounded by a Keplerian disk and an infalling envelope. The Keplerian disk is clearly identified in the 12CO and C18O emission, and its outer radius (∼700 AU) and mass (∼0.005 M☉) are comparable to those of disks around T Tauri stars. The protostellar mass is estimated to be 1.6 M☉ with the inclination angle of 66°. In addition to the Keplerian disk, there are blueshifted and redshifted off-axis protrusions seen in the C18O emission pointing toward the north and the south, respectively, adjunct to the middle part of the Keplerian disk. The shape and kinematics of these protrusions can be interpreted as streams of infalling flows with a conserved angular momentum following parabolic trajectories toward the Keplerian disk, and the mass infalling rate is estimated to be ∼5 × 10−7 M☉ yr−1. The specific angular momentum of the infalling flows (∼2.5 × 10−3 km s−1 pc) is comparable to that at the outer radius of the Keplerian disk (∼4.8 × 10−3 km s−1 pc). The SO emission is elongated along the disk major axis and exhibits a linear velocity gradient along the axis, which is interpreted to mean that the SO emission primarily traces a ring region in the flared Keplerian disk at radii of ∼250–390 AU. The local enhancement of the SO abundance in the ring region can be due to the accretion shocks at the centrifugal radius where the infalling flows fall onto the disk. Our ALMA observations unveiled both the Keplerian disk and the infalling gas onto the disk, and the disk can further grow by accreting material and angular momenta from the infalling gas.
Deep Learning Based Calibration Time Reduction for MOS Gas Sensors with Transfer Learning

In this study, methods from the field of deep learning are used to calibrate a metal oxide semiconductor (MOS) gas sensor in a complex environment in order to be able to predict a specific gas concentration. Specifically, we want to tackle the problem of long calibration times and the problem of transferring calibrations between sensors, which is a severe challenge for the widespread use of MOS gas sensor systems. Therefore, this contribution aims to significantly diminish those problems by applying transfer learning from the field of deep learning. Within the field of deep learning, transfer learning has become more and more popular. Nowadays, building a model (calibrating a sensor) based on pre-trained models instead of training from scratch is a standard routine. This allows the model to train with inherent information and reach a suitable solution much faster or more accurately. For predicting the gas concentration with a MOS gas sensor operated dynamically using temperature cycling, the calibration time can be significantly reduced for all nine target gases at the ppb level (seven volatile organic compounds plus carbon monoxide and hydrogen). It was possible to reduce the calibration time by up to 93% and still obtain root-mean-squared error (RMSE) values only double the best achieved RMSEs. In order to obtain the best possible transferability, different transfer methods and the influence of different transfer data sets for training were investigated. Finally, transfer learning based on neural networks is compared to a global calibration model based on feature extraction, selection, and regression to place the results in the context of already existing work.
FTPS: Efficient fault‐tolerant dynamic phrase search over outsourced encrypted data with forward and backward privacy

With the popularity of cloud computing, more and more users store sensitive information in cloud servers. In order to protect the data over the cloud server, symmetric encryption with keyword search has been developed and the phrase search has been proposed subsequently to overcome the inefficiency produced by single/multi‐keyword search. However, most existing phrase search schemes fails to support fault‐tolerant search which is essential to users. Therefore, this paper proposes a fault‐tolerant dynamic phrase search scheme with forward privacy and backward privacy (FTPS). Piecewise Linear Chaotic Map and minhash function are used to blur information, and Bloom filter based index is constructed to realize efficient search and dynamic update simultaneously. Security analysis proves that FTPS can properly preserve the privacy of search user, and experimental results show that FTPS is practical.
The Spitzer South Pole Telescope Deep-Field Survey: Linking galaxies and haloes at z = 1.5

We present an analysis of the clustering of high-redshift galaxies in the recently completed 94 deg$^2$ Spitzer-SPT Deep Field survey. Applying flux and color cuts to the mid-infrared photometry efficiently selects galaxies at $z\sim1.5$ in the stellar mass range $10^{10}-10^{11}M_\odot$, making this sample the largest used so far to study such a distant population. We measure the angular correlation function in different flux-limited samples at scales $>6^{\prime \prime}$ (corresponding to physical distances $>0.05$ Mpc) and thereby map the one- and two-halo contributions to the clustering. We fit halo occupation distributions and determine how the central galaxy's stellar mass and satellite occupation depend on the halo mass. We measure a prominent peak in the stellar-to-halo mass ratio at a halo mass of $\log(M_{\rm halo} / M_\odot) = 12.44\pm0.08$, 4.5 times higher than the $z=0$ value. This supports the idea of an evolving mass threshold above which star formation is quenched. We estimate the large-scale bias in the range $b_g=2-4$ and the satellite fraction to be $f_\mathrm{sat}\sim0.2$, showing a clear evolution compared to $z=0$. We also find that, above a given stellar mass limit, the fraction of galaxies that are in similar mass pairs is higher at $z=1.5$ than at $z=0$. In addition, we measure that this fraction mildly increases with the stellar mass limit at $z=1.5$, which is the opposite of the behavior seen at low-redshift.
Development of Expert System for AOSpine Thoracolumber Spine Injury Classification: An Application for Healthcare Providers

This article presents the development of expert system application that mimics the steps done by healthcare providers who are responsible for determining the type of spine injury by using the algorithm of AOSpine thoracolumbar spine injury classification. The expert system is capable of recommending the type of spine fracture; this recommendation done by the proposed expert system will facilitate the selection of treatment and referral of the patients to the specialized healthcare unit. The source of knowledge used in the proposed expert system was collected from various related literature studies and spine surgeons. The proposed expert system was evaluated by a group of spine surgeons at the spine unit, College of Medicine, Zagazig University, Egypt. The proposed expert system was then evaluated in terms of its functionality, usability, and reliability using the ISO/IEC 9126 software quality standards. The results showed that the proposed expert system was able to achieve the defined objectives of this study.
Automated segmentation of multiple sclerosis lesions based on convolutional neural networks

ABSTRACT Automatic segmentation using Deep Learning (DL) has recently outperformed previous methods. In this paper, we are interested in automatic MS lesion segmentation of 2D images using DL approaches. The main challenge is to propose a new model that uses referenced CNN models such as U-Net, ResNet, and DenseNet, but with less parameters and a faster execution time. We evaluated the performance of our suggested technique, ‘Concat-U-Net’, to that of three implemented models, using two different datasets: our private MS database and the public ISBI. A comparison research was conducted using the Dice, Sensitivity, Precision, and PR-Curve evaluation metrics. On our private data set, the best Dice produced by the suggested technique was 0.80 as well as 0.77, 0.98, and 0.78 for sensitivity, PR-curve, and Precision, respectively. We enhanced MS segmentation results using the ISBI database and acquired a Dice value of 0.72, which is close to the results reported in the literature. For our data set and ISBI database, we lowered the elapsed execution time from 16.99 s to 9.36 s and from 25.02 s to 4.37 s, respectively. Furthermore, by reducing the number of parameters, we achieved an 81.51% time gain.
Hyperfine excitation of N2H+ by H2: towards a revision of N2H+ abundance in cold molecular clouds

The modelling of emission spectra of molecules seen in interstellar clouds requires the knowledge of collisional rate coefficients. Among the commonly observed species, N$_2$H$^+$ is of particular interest since it was shown to be a good probe of the physical conditions of cold molecular clouds. Thus, we have calculated hyperfine-structure resolved excitation rate coefficients of N$_2$H$^+$(X$^1\Sigma^+$) by H$_2(j=0)$, the most abundant collisional partner in the cold interstellar medium. The calculations are based on a new potential energy surface, obtained from highly correlated {\it ab initio} calculations. State-to-state rate coefficients between the first hyperfine levels were calculated, for temperatures ranging from 5 K to 70 K. By comparison with previously published N$_2$H$^+$-He rate coefficients, we found significant differences which cannot be reproduced by a simple scaling relationship. As a first application, we also performed radiative transfer calculations, for physical conditions typical of cold molecular clouds. We found that the simulated line intensities significantly increase when using the new H$_2$ rate coefficients, by comparison with the predictions based on the He rate coefficients. In particular, we revisited the modelling of the N$_2$H$^+$ emission in the LDN 183 core, using the new collisional data, and found that all three of the density, gas kinetic temperature and N$_2$H$^+$ abundance had to be revised.
A model of spatial location: New data for the Gor River megalithic landscape (Spain) from LiDAR technology and field survey

The megalithic cluster of the Gor River valley (Andalusia, Spain) is one of the biggest dolmenic groups in Europe, made up of 151 preserved megaliths. In spite of this high number of known monuments, increasing loss and destruction of many of the graves has taken place during the last decades due to enormous soil erosion and anthropogenic activities. With the aim of recording the location of these lost megaliths, Digital Terrain Models and LiDAR data have been used to analyse the terrain showing a high quantity of structures that seem similar to those actually documented in the zone but that were not noticed until now. These possible new burial mounds have been tested by archaeological surface survey, choosing three contrasting areas as samples. Results have shown a high success rate for this methodology, even allowing the discovery of new megalithic graves in heavily researched areas. We interpret the likely higher number of burial mounds in the area to indicate greater territorial control in boundary areas between 4th and 3rd millennium BC.