Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanism of Action of Streptococcus downii, a New Bacterial Species with Probiotic Potential

Streptococcus downii is a recently reported bacterial species of oral origin, with inhibitory capacity against Streptococcus mutans, Actinomyces naeslundii, Veillonella parvula and Aggregatibacter actinomycetemcomitans, which confers upon it the potential of being an oral probiotic. The aim of the present study was to identify the potential mechanisms by which S. downii exerts its inhibitory effect on S. mutans. To this end, the study assessed the consumption of glucose and proteins available in the culture medium, the modification of the pH, the production of short-chain fatty acids, the changes in the protein panel of the inhibition halo, the production of hydrogen peroxide and the effect of proteinase K. There were no differences in the glucose values or in the protein content of the medium, but there was a reduction in pH (with no effect on the growth of S. mutans). Significant increases were detected in the levels of lactic and formic acid (with no effect on the growth of S. mutans), as well as changes in the peptide panel (with no effect on the growth of S. mutans). The inhibitory effect was maintained in the presence of peroxidase but disappeared after adding proteinase K. Based on these results, it is suggested that the main mechanism of inhibition of S. downii against S. mutans is the production of bacteriocins.
The Roles and Modes of Human Interactions with Automated Machine Learning Systems: A Critical Review and Perspectives

As automated machine learning (AutoML) systems continue to progress in both sophistication and performance, it becomes important to understand the ‘how’ and ‘why’ of human-computer interaction (HCI) within these frameworks, both current and expected. Such a discussion is necessary for optimal system design, leveraging advanced data-processing capabilities to support decision-making involving humans, but it is also key to identifying the opportunities and risks presented by ever-increasing levels of machine autonomy. Within this context, we focus on the following questions: (i) What does HCI currently look like for state-of-the-art AutoML algorithms, especially during the stages of development, deployment, and maintenance? (ii) Do the expectations of HCI within AutoML frameworks vary for different types of users and stakeholders? (iii) How can HCI be managed so that AutoML solutions acquire human trust and Thanh Tung Khuat, David Jacob Kedziora and Bogdan Gabrys (2023), “The Roles and Modes of Human Interactions with Automated Machine Learning Systems: A Critical Review and Perspectives”, Foundations and Trends® in Human-Computer Interaction: Vol. 17, No. 3-4, pp 195–387. DOI: 10.1561/1100000091. ©2023 T. T. Khuat et al. Full text available at: http://dx.doi. org/10.1561/1100000091
A Kullback-Liebler divergence-based representation algorithm for malware detection

Background Malware, malicious software, is the major security concern of the digital realm. Conventional cyber-security solutions are challenged by sophisticated malicious behaviors. Currently, an overlap between malicious and legitimate behaviors causes more difficulties in characterizing those behaviors as malicious or legitimate activities. For instance, evasive malware often mimics legitimate behaviors, and evasion techniques are utilized by legitimate and malicious software. Problem Most of the existing solutions use the traditional term of frequency-inverse document frequency (TF-IDF) technique or its concept to represent malware behaviors. However, the traditional TF-IDF and the developed techniques represent the features, especially the shared ones, inaccurately because those techniques calculate a weight for each feature without considering its distribution in each class; instead, the generated weight is generated based on the distribution of the feature among all the documents. Such presumption can reduce the meaning of those features, and when those features are used to classify malware, they lead to a high false alarms. Method This study proposes a Kullback-Liebler Divergence-based Term Frequency-Probability Class Distribution (KLD-based TF-PCD) algorithm to represent the extracted features based on the differences between the probability distributions of the terms in malware and benign classes. Unlike the existing solution, the proposed algorithm increases the weights of the important features by using the Kullback-Liebler Divergence tool to measure the differences between their probability distributions in malware and benign classes. Results The experimental results show that the proposed KLD-based TF-PCD algorithm achieved an accuracy of 0.972, the false positive rate of 0.037, and the F-measure of 0.978. Such results were significant compared to the related work studies. Thus, the proposed KLD-based TF-PCD algorithm contributes to improving the security of cyberspace. Conclusion New meaningful characteristics have been added by the proposed algorithm to promote the learned knowledge of the classifiers, and thus increase their ability to classify malicious behaviors accurately.
High-throughput and separating-free phenotyping method for on-panicle rice grains based on deep learning

Rice is a vital food crop that feeds most of the global population. Cultivating high-yielding and superior-quality rice varieties has always been a critical research direction. Rice grain-related traits can be used as crucial phenotypic evidence to assess yield potential and quality. However, the analysis of rice grain traits is still mainly based on manual counting or various seed evaluation devices, which incur high costs in time and money. This study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology, which can achieve high-throughput extraction of critical traits of rice panicles without separating and threshing rice panicles. The imaging of rice panicles was realized through visible light scanning. The grains were detected and segmented using the Faster R-CNN-based model, and an improved Pix2Pix model cascaded with it was used to compensate for the information loss caused by the natural occlusion between the rice grains. An image processing pipeline was designed to calculate fifteen phenotypic traits of the on-panicle rice grains. Eight varieties of rice were used to verify the reliability of this method. The R2 values between the extraction by the method and manual measurements of the grain number, grain length, grain width, grain length/width ratio and grain perimeter were 0.99, 0.96, 0.83, 0.90 and 0.84, respectively. Their mean absolute percentage error (MAPE) values were 1.65%, 7.15%, 5.76%, 9.13% and 6.51%. The average imaging time of each rice panicle was about 60 seconds, and the total time of data processing and phenotyping traits extraction was less than 10 seconds. By randomly selecting one thousand grains from each of the eight varieties and analyzing traits, it was found that there were certain differences between varieties in the number distribution of thousand-grain length, thousand-grain width, and thousand-grain length/width ratio. The results show that this method is suitable for high-throughput, non-destructive, and high-precision extraction of on-panicle grains traits without separating. Low cost and robust performance make it easy to popularize. The research results will provide new ideas and methods for extracting panicle traits of rice and other crops.
Earth Radiation Budget Experiment (ERBE) Data Sets for Global Environment and Climate Change Studies

For a number of years there has been considerable interest in the earth's radiation budget (ERB) or energy balance, and entails making the best measurements possible of absorbed solar radiation, reflected shortwave radiation (RSW), thermal outgoing longwave radiation (OLR), and net radiation. ERB data are fundamental to the development of realistic climate models and studying natural and anthropogenic perturbations of the climate. Much of the interest and investigations in the earth's energy balance predated the age of earth-orbiting satellites (Hunt et al., 1986). Beginning in the mid 1960's earth-orbiting satellites began to play an important role in making measurements of the earth's radiation flux although much effort had gone into measuring ERB parameters prior to 1960 (House et al., 1986). Beginning in 1974 and extending until the present time, three different satellite experiments (not all operating at the same time) have been making radiation budget measurements almost continually in time. Two of the experiments were totally dedicated to making radiation budget measurements of the earth, and the other experiment flown on NOAA sun-synchronous AVHRR weather satellites produced radiation budget parameters as a by-product. The heat budget data from the AVHRR satellites began collecting data in June 1974 and have operated almost continuously for 23 years producing valuable data for long term climate monitoring.
Epidemiology of tuberculosis patients at Mithamain Upazila, Kishoreganj district, Bangladesh

Bangladesh is a hotspot for TB infection. The COVID-19 pandemic has impacted many aspects of healthcare, including TB control programs. Our retrospective study was conducted with a total of 6873 sputum samples from a primary-level healthcare facility in Upazila Health Complex in Mithamain Upazila, Kishoreganj District, Bangladesh over a period from January 2020 to December 2022.The sputum sample was tested by acid-fast bacilli (AFB) staining. Among them, 3.08%, 2.53% and 2.03% patients were TB positive in the years 2020, 2021 and 2022, respectively. Positive results were higher in 25-44 years of men. No significant correlation with the incidence of TB cases in certain month was found appropriate. Farmers were found at the highest threat in in acquiring TB. By monitoring TB rates over time, public health officials can assess the impact of interventions and adjust strategies as needed to achieve the goal of ending the TB epidemic by 2030.
Anti-Jamming Null Space Projection Beamforming Based on Symbiotic Radio

With the development of information technology, more and more devices are connected to the Internet through wireless communication to complete data interconnection. Due to the broadcast characteristics of wireless channels, wireless networks have suffered more and more malicious attacks. Physical layer security has received extensive attention from industry and academia. MIMO is considered to be one of the most important technologies related to physical layer security. Through beamforming technology, messages can be transmitted to legitimate users in an offset direction that is as orthogonal as possible to the interference channel to ensure the reception SINR by legitimate users. Combining the symbiotic radio (SR) technology, this paper considers a symbiotic radio anti-jamming MIMO system equipped with a multi-antenna system at the main base station. In order to avoid the interference signal and improve the SINR of the signal received by the user. The base station is equipped with a uniform rectangular antenna array, and using Null Space Projection (NSP) Beamforming, Intelligent Reflecting Surface (IRS) can assist in changing the beam’s angle. The simulation results show that NSP Beamforming could make a better use of the null space of interference, which can effectively improve the received SINR of users under directional interference, and improve the utilization efficiency of signal energy.
Organoruthenium Complexes Containing Phosphinodicarboxamide Ligands

Ruthenium complexes of phosphinocarboxamide ligands, and their use to form metallacycles using halide abstraction/deprotonation reactions are reported. Thus, [Ru(p-cym){PPh2C(=O)NHR}Cl2; R = iPr (1), Ph (2), p-tol (3)] and [Ru(p-cym){PPh2C(=O)N(R)C(=O)N(H)R}Cl2; R = Ph (4), p-tol (5)] were synthesized from [(p-cym)RuCl2]2 (p-cym = para-cymene) and phosphinocarboxamides or phosphinodicarboxamides, respectively. Single-crystal X-ray diffraction measurements on 1–5 reveal coordination to ruthenium through the phosphorus donor, with an intramolecular hydrogen bond between the amine-bound proton and a metal-bound chloride. Six-membered metallacycles formed by halide abstraction/deprotonation of complexes 4 and 5 afforded [Ru(p-cym){κ2-P,N-PPh2C(=O)N(R)C(=O)NR}Cl] [R = Ph (6), p-tol (7)]. These species exist as a mixture of two rotational isomers in solution, as demonstrated by NMR spectroscopy.
The Universality of the Normalized Vertical Velocity Variance in Contrast to the Horizontal Velocity Variance in the Convective Boundary Layer

The vertical turbulent velocity variance normalized by the convective velocity squared as a function of the boundary layer depth–normalized height [i.e., ] in the convective boundary layer (CBL) over a homogeneous surface exhibits a near-universal profile, as demonstrated by field observations, laboratory experiments, and numerical simulations. The profile holds over a wide CBL stability range set by the friction velocity, CBL depth, and surface heating. In contrast, the normalized horizontal turbulent velocity variance increases monotonically with decreasing stability. This study investigates the independence of the profile to changes in CBL stability, or more precisely, wind shear. Large-eddy simulations of several convective and neutral cases are performed by varying surface heating and geostrophic winds. Analysis of the turbulent kinetic energy budgets reveals that the conversion term between and depends almost entirely on buoyancy. This explains why does not vary with shear, which is a source to only. Further analysis through rotational and divergent decomposition suggests that the near-universal profile of is fundamentally related to the dynamics and interactions of local and nonlocal CBL turbulence. Specifically, the preferential interactions between local wavenumbers and the downscale energy cascade of CBL turbulence offer plausible explanations to the universal profile of .
Navigating Challenges in the Management of Mandibular Third Molars With Radix Paramolaris: A Case Report

This study aims to shed light on a contemporary approach to preserving third molars instead of opting for immediate extraction. Third molars are known for their diverse shapes and unique anatomy, making root canal treatment a complex task due to limited access. However, there are situations where it is crucial to retain these molars, such as when they provide support or for self-transplantation purposes. The case report focuses on a 33-year-old female patient who presented with pulp necrosis and acute apical periodontitis in the lower right third molar. Instead of extraction, a two-visit conventional root canal treatment was planned. During the initial diagnostic radiographs, only two visible roots were observed, illustrating the typical anatomy of the third molar. However, an unforeseen additional root, referred to as radix paramolaris, was encountered in the mesiobuccal region during the access opening, presenting numerous challenges in the treatment process. Thankfully, advancements in dental technology, such as magnification aids, ultrasonic tips and flexible nickel-titanium (NiTi) rotary files, have rendered the management of such intricate cases more attainable. In conclusion, dealing with intricately curved canals in difficult-to-reach teeth like third molars has become more achievable with technological progress, although the operator's skill and experience remain crucial for effective management.
Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification

Radio signal classification has a very wide range of applications in the field of wireless communications and electromagnetic spectrum management. In recent years, deep learning has been used to solve the problem of radio signal classification and has achieved good results. However, the radio signal data currently used are very limited in scale. In order to verify the performance of the deep learning-based radio signal classification on real-world radio signal data, in this paper, we conduct experiments on large-scale real-world ACARS and ADS-B signal data with sample sizes of 900 000 and 13 000 000,, respectively, and with categories of 3143 and 5157, respectively. We use the same inception-residual neural network model structure for ACARS signal classification and ADS-B signal classification to verify the ability of a single basic deep neural network model structure to process different types of radio signals, i.e., communication bursts in ACARS and pulse bursts in ADS-B. We build an experimental system for radio signal deep learning experiments. The experimental results show that the signal classification accuracy of ACARS and ADS-B is 98.1% and 96.3%, respectively. When the signal-to-noise ratio (with injected additive white Gaussian noise) is greater than 9 dB, the classification accuracy is greater than 92%. These experimental results validate the ability of deep learning to classify large-scale real-world radio signals. The results of the transfer learning experiment show that the model trained on large-scale ADS-B datasets is more conducive to the learning and training of new tasks than the model trained on small-scale datasets.
Computer-Aided Study of Double Extensions of Restricted Lie Superalgebras Preserving the Nondegenerate Closed 2-Forms in Characteristic 2

Abstract A Lie (super)algebra with a nondegenerate invariant symmetric bilinear form B is called a nis-(super)algebra. The double extension of a nis-(super)algebra is the result of simultaneous adding to a central element and a derivation so that is a nis-algebra. Loop algebras with values in simple complex Lie algebras are most known among the Lie (super)algebras suitable to be doubly extended. In characteristic 2, the notion of double extension acquires specific features. Restricted Lie (super)algebras are among the most interesting modular Lie superalgebras. In characteristic 2, using Grozman’s Mathematica-based package SuperLie, we list double extensions of restricted Lie superalgebras preserving the nondegenerate closed 2-forms with constant coefficients. The results are proved for the number of indeterminates ranging from 4 to 7—sufficient to conjecture the pattern for larger numbers. Considering multigradings allowed us to accelerate computations up to 100 times.
A Multi-Layer Breast Cancer Model to Study the Synergistic Effect of Photochemotherapy

Breast cancer is one of the most common cancers among women. The development of new and effective therapeutic approaches in the treatment of breast cancer is an important challenge in modern oncology. Two-dimensional (2D) cell cultures are most often used in the study of compounds with potential anti-tumor nature. However, it is necessary to develop advanced three-dimensional (3D) cell models that can, to some extent, reflect the physiological conditions. The use of miniature cancer-on-a-chip microfluidic systems can help to mimic the complex cancer microenvironment. In this report, we developed a 3D breast cancer model in the form of a cell multilayer, composed of stromal cells (HMF) and breast cancer parenchyma (MCF-7). The developed cell model was successfully used to analyze the effectiveness of combined sequential photochemotherapy, based on doxorubicin and meso-tetraphenylporphyrin. We proved that the key factor that allows achieving the synergistic effect of combination therapy are the order of drug administration to the cells and the sequence of therapeutic procedures. To the best of our knowledge, studies on the effectiveness of combination photochemotherapy depending on the sequence of the component drugs were performed for the first time under microfluidic conditions on a 3D multilayered model of breast cancer tissue.
Assessment of the Immunoprotective Efficacy of Recombinant 14-3-3 Protein and Dense Granule Protein 10 (GRA10) as Candidate Antigens for Rabbit Vaccines against Eimeria intestinalis

Eimeria intestinalis infects rabbits, causing severe intestinal coccidiosis. Prolonged anticoccidial drug use might lead to coccidia resistance and drug residues in food. Thus, vaccines are required to control rabbit coccidiosis. In this study, recombinant E. intestinalis 14-3-3 and GRA10 proteins (rEi-14-3-3 and rEi-GRA10) were obtained via prokaryotic expression and used as recombinant subunit vaccines. Fifty 30-day-old rabbits were randomly grouped as follows: PBS-uninfected group, PBS-infected group, Trx-His-S control group, and rEi-14-3-3 and rEi-GRA10 immunized groups. The rabbits were subcutaneously immunized twice at 2-week intervals, challenged with 7 × 104 sporulated oocysts, and sacrificed 14 days later. The protective effects were assessed via clinical signs, relative weight gain, oocyst reduction, mean intestinal lesion score, ACI (anticoccidial index), cytokine, and specific antibody levels in sera. The rEi-14-3-3 and rEi-GRA10 groups had higher relative weight gain rates of 81.94% and 73.61% (p < 0.05), and higher oocyst reduction rates of 86.13% and 84.87% (p < 0.05), respectively. The two immunized groups had fewer intestinal lesions (p < 0.05) and higher IgG levels (p < 0.05). Higher levels of IL-2, IL-4, and IFN-γ cytokines in the rEi-14-3-3 group (p < 0.05) and a higher level of IFN-γ in the rEi-GRA10 group (p < 0.05) were observed. The ACI values of the rEi-14-3-3 and rEi-GRA10 groups were 168.24 and 159.91, with good and moderate protective effects, respectively. Both rEi-14-3-3 and rEi-GRA10 induced humoral immunity in the rabbits. In addition, rEi-14-3-3 induced Th1- and Th2-type immune responses. Both recombinant proteins were protective against E. intestinalis infection in rabbits, with rEi-14-3-3 showing a better protective effect.
Asymptotic analysis of synchrosqueezing transform -- toward statistical inference with nonlinear-type time-frequency analysis

We provide a statistical analysis of a tool in nonlinear-type time-frequency analysis, the synchrosqueezing transform (SST), for both the null and non-null cases. The intricate nonlinear interaction of different quantities in SST is quantified by carefully analyzing relevant multivariate complex Gaussian random variables. Specifically, we provide the quotient distribution of dependent and improper complex Gaussian random variables. Then, a central limit theorem result for SST is established. {As an example}, we provide a block bootstrap scheme based on the established SST theory to test if a given time series contains oscillatory components.
The Formation of Attitudes Toward Cross-Border Shopping Websites

This study aimed to explore the effects of perceived benefits on consumers' attitudes towards cross-border online shopping websites. We also explore whether and how consumers' COVID-19 anxiety and brand familiarity weaken or strengthen the relationship between these perceived benefits and consumer attitudes. A total 319 items of data were used for the final analysis. The perceived benefits of a website were found to have only an indirect effect on purchase intentions by mediating consumers' attitudes toward the site. Competitive pricing of fashion products directly affected purchase intentions. COVID-19 anxiety was found to have a negative moderating influence on the relationship between perceived enjoyment and consumer attitudes, whereas brand familiarity had a positive moderating effect on both the relationships between perceived usefulness and consumers' attitudes toward the site and between perceived ease of use and their site attitude. This study provides useful insights for international e-tailers in developing marketing strategies that attract international consumers. Academically, we have contributed to the existing literature on the perceived benefits of global online shopping and the moderators of consumers' attitudes towards e-commerce sites.