Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing Polarity and pH Sensitivity of Carbon Dots in Escherichia coli through Time-Resolved Fluorescence Analyses

Intracellular monitoring of pH and polarity is crucial for understanding cellular processes and functions. This study employed pH- and polarity-sensitive nanomaterials such as carbon dots (CDs) for the intracellular sensing of pH, polarity, and viscosity using integrated time-resolved fluorescence anisotropy (FA) imaging (TR-FAIM) and fluorescence lifetime (FLT) imaging microscopy (FLIM), thereby enabling comprehensive characterization. The functional groups on the surface of CDs exhibit sensitivity to changes in the microenvironment, leading to variations in fluorescence intensity (FI) and FLT according to pH and polarity. The FLT of CDs in aqueous solution changed gradually from 6.38 ± 0.05 ns to 8.03 ± 0.21 ns within a pH range of 2–8. Interestingly, a complex relationship of FI and FLT was observed during measurements of CDs with decreasing polarity. However, the FA and rotational correlation time (θ) increased from 0.062 ± 0.019 to 0.112 ± 0.023 and from 0.49 ± 0.03 ns to 2.01 ± 0.27 ns, respectively. This increase in FA and θ was attributed to the higher viscosity accompanying the decrease in polarity. Furthermore, CDs were found to bind to three locations in Escherichia coli: the cell wall, inner membrane, and cytoplasm, enabling intracellular characterization using FI and FA decay imaging. FLT provided insights into cytoplasmic pH (7.67 ± 0.48), which agreed with previous works, as well as the decrease in polarity in the cell wall and inner membrane. The CD aggregation was suspected in certain areas based on FA, and the θ provided information on cytoplasmic heterogeneity due to the aggregation and/or interactions with biomolecules. The combined TR-FAIM/FLIM system allowed for simultaneous monitoring of pH and polarity changes through FLIM and viscosity variations through TR-FAIM.
Pedestrian segmentation in infrared images based on local autocorrelation

In order to select the optimal threshold for pedestrian segmentation in infrared images, a novel algorithm based on local autocorrelation is proposed. The algorithm calculates the local autocorrelation feature of a given image. Next, it constructs a new feature matrix based on this spatial correlation and the original grayscale. Then, it obtains an automatic threshold related with local combined features using the geometrical method based on histogram analysis. Finally, it extracts the image region of pedestrian and yields the binary result. It is indicated by the experiments that, the proposed method performs good result of pedestrian region extraction and thresholding, and it is reasonable and effective.
Performance investigation of bridge-less power factor correction circuit with MOSFET

The aim of this paper is to study and investigate the performance of power factor correction (PFC) circuit implemented with the semi bridge-less configuration. The transistor-diode module APT50N60JCCU2 has been used in the proposed circuit and the UCC28070 controller has been used as a controlling device for the power factor correction (PFC) circuit. Testing has been performed in two steps. In the first step, the test was conducted on (230 Vac), while in the second step, the test was conducted on (115 Vac) as alternating input voltages. The testing results of both voltages were compared and analyzed in terms of efficiency, power factor, total harmonic distortion (THD) in order to determine the efficiency of the power factor correction circuit. The results obtained indicate that this circuit has efficiency up to 97% and a power factor close to 0.91 with the input voltage of 230 V.
Comparing Data Store Performance for Full-Text Search: To SQL or to NoSQL?

: The amount of textual data produced nowadays is constantly increasing as the number and variety of both new and reproduced textual information created by humans and (lately) also by bots is unprecedented. Storing, handling and querying such high volumes of textual data have become more challenging than ever and both research and industry have been using various alternatives, ranging from typical Relational Database Management Systems to specialised text engines and NoSQL databases, in an effort to cope with the volume. However, all these decisions are, largely, based on experience or personal preference for one system over another, since there is no performance comparison study that compares the available solutions regarding full-text search and retrieval. In this work, we ﬁll this gap in the literature by systematically comparing four popular databases in full-text search scenarios and reporting their performance across different datasets, full-text search operators and parameters. To the best of our knowledge, our study is the ﬁrst to go beyond the comparison of characteristics, like expressiveness of the query language or popularity, and actually compare popular relational, NoSQL, and textual data stores in terms of retrieval efﬁciency for full-text search. Moreover, our ﬁndings quantify the differences in full-text search performance between the examined solutions and reveal both anticipated and less anticipated results.
Neuropathology in COVID-19 autopsies is defined by microglial activation and lesions of the white matter with emphasis in cerebellar and brain stem areas

Introduction This study aimed to investigate microglial and macrophage activation in 17 patients who died in the context of a COVID-19 infection in 2020 and 2021. Methods Through immunohistochemical analysis, the lysosomal marker CD68 was used to detect diffuse parenchymal microglial activity, pronounced perivascular macrophage activation and macrophage clusters. COVID-19 patients were compared to control patients and grouped regarding clinical aspects. Detection of viral proteins was attempted in different regions through multiple commercially available antibodies. Results Microglial and macrophage activation was most pronounced in the white matter with emphasis in brain stem and cerebellar areas. Analysis of lesion patterns yielded no correlation between disease severity and neuropathological changes. Occurrence of macrophage clusters could not be associated with a severe course of disease or preconditions but represent a more advanced stage of microglial and macrophage activation. Severe neuropathological changes in COVID-19 were comparable to severe Influenza. Hypoxic damage was not a confounder to the described neuropathology. The macrophage/microglia reaction was less pronounced in post COVID-19 patients, but detectable i.e. in the brain stem. Commercially available antibodies for detection of SARS-CoV-2 virus material in immunohistochemistry yielded no specific signal over controls. Conclusion The presented microglial and macrophage activation might be an explanation for the long COVID syndrome.
The Collagen-Based Scaffolds for Bone Regeneration: A Journey through Electrospun Composites Integrated with Organic and Inorganic Additives

Orthopedics has been identified as a major clinical medicine branch since the 18th century for musculoskeletal disease diagnosis and therapeutics. Along with technological progress, the surgical treatment of bone disorders became available in the 19th century, while its growth faced several obstacles due to a lack of proper biocompatible material and alternative structures. Therefore, tissue engineering has emerged as a key building block to overcome these challenges, providing the capability for bone growth, and fabricating scaffolds with enriched desirable cellular compatibility as well as mechanical properties. Among various structures, the electrospun layer has implied high porosity and fine pore sizes, and succeeded in cell growth and proliferation. Collagen nanofibers have represented a wide potential for mineralization, bone regeneration, and forming processes. Despite this, such scaffolds have accosted bone remodeling limitations due to inadequate osteoinductivity and mechanical strength. Hence, the tendency to fabricate efficient collagen-based nanofibrous layers enriched with organic and inorganic materials has been extensively declared. Embedding these materials leads to engineering a membrane with appropriate physical, degradability, and mechanical properties, as well as proper mineralization and biological activity required for better replicating the bone organ’s natural microenvironment. This paper highlighted a wide overview of the natural resources, electrospinning strategies, and collagen-based electrospun composites for bone regeneration. Accordingly, future prospects could be developed for generating novel 3D-scaffold formations, benefiting from organic and inorganic substances to boost the biological and mechanical properties, simultaneously.
Antibacterial Properties of Carvacrol against Antibiotic- Resistant Bacteria, Enteric Bacteria, and Oral Pathogens

: The present study investigated the antibacterial activity of carvacrol against Vancomycin-Resistant Enterococci (VRE), Carbapenem-Resistant Enterobacteriaceae (CRE), Methicillin-Resistant Staphylococcus aureus (MRSA), Escherichia coli ATCC 8739, Enterococcus faecalis ATCC 29212, Streptococcus mitis , Streptococcus mutans CNCTC 8/77 and Streptococcus salivarius CNCTC 64/59. According to the results of the disc diffusion method, carvacrol has extremely high antibacterial activity with zones of 28 mm, 38 mm, 39 mm, 36 mm, 50 mm, 41 mm, 47 mm, and 39 mm against E. coli, E. faecalis , VRE, CRE, S. salivarius, S. mutans, MRSA , S. mitis , respectively. In the present study, the Minimum Inhibition Concentration (MIC) was also investigated by using the Broth Microdilution method. Carvacrol has MIC of 7.62 mg/mL against E. coli , 15.25 mg/mL against VRE, 30.5 mg/mL against E. faecalis , CRE, and S. salivarius , 61 mg/mL against S. mutans , 1.90 mg/mL against MRSA and S. mitis. Carvacrol is a very strong antimicrobial agent and may be used in important applications in the food and pharmaceutical industry. It may also be a good candidate for developing alternative treatments. The results of the present study may be beneficial for further studies with this antibacterial agent for both academia and industry.
A Comprehensive Overview of the Utilization of Recycled Waste Materials and Technologies in Asphalt Pavements: Towards Environmental and Sustainable Low-Carbon Roads

Given the prevailing concerns about greenhouse gas emissions, global warming, and the growing demand for renewable resources, the pavement industry, among others, is actively engaged in researching and exploring low-carbon materials and technologies. Despite the growing interest in low-carbon asphalt pavement, there is still a significant knowledge gap regarding the use of various waste materials and technologies to achieve this goal. This study aims to close this gap by conducting a systematic review and scientometric assessment of the existing studies on the use of waste materials and technologies for low-carbon asphalt pavement. The study spans the years 2008 to 2023, and the scientometric analysis was conducted using the VOS viewer application. The study identifies materials and technologies in this area by examining co-authored country studies, publication sources, and keyword co-occurrence. It should be noted that a limited number of waste materials that allow CO2 emissions reduction was analyzed in this study. However, other waste categories, such as bio-oils and polymers, which can provide positive either environmental or economic impacts on the production of paving materials, were not considered in the scope of the study. Based on the current review, it was found that integrating recycled waste materials like recycled asphalt pavement, biochar, or crumb rubber with alternative mixing technologies such as warm mix asphalt and cleaner energy can significantly reduce CO2 emissions. China and the United States were identified as key research contributors to the low-carbon pavement. Furthermore, biomass-based fuel and electric construction equipment lower carbon and greenhouse gas emissions by 36–90% and 67–95%, respectively. However, before various recycled waste materials and technologies can be widely used in the asphalt industry, various challenges need to be addressed, including cost concerns, performance and durability concerns, standardization and regulations, availability, integration with existing facilities, and insufficient field and long-term data. The review identified critical research gaps, such as the absence of a homogeneous and reliable standard method for low-carbon asphalt pavement, limited field performance data, and a life cycle assessment approach in analyzing the emission reduction effects. The reviews will aid in the paradigm shift to a more carbon-friendly pavement industry that uses recycled waste materials and technologies.
Early Splicing Complexes and Human Disease

Over the last decade, our understanding of spliceosome structure and function has significantly improved, refining the study of the impact of dysregulated splicing on human disease. As a result, targeted splicing therapeutics have been developed, treating various diseases including spinal muscular atrophy and Duchenne muscular dystrophy. These advancements are very promising and emphasize the critical role of proper splicing in maintaining human health. Herein, we provide an overview of the current information on the composition and assembly of early splicing complexes—commitment complex and pre-spliceosome—and their association with human disease.
Directed Percolation: Calculation of Feynman Diagrams in the Three-Loop Approximation

The directed bond percolation process is an important model in statistical physics. By now its universal properties are known only up to the second-order of the perturbation theory. Here, our aim is to put forward a numerical technique with anomalous dimensions of directed percolation to higher orders of perturbation theory and is focused on the most complicated Feynman diagrams with problems in calculation. The anomalous dimensions are computed up to three-loop order in ε = 4 − d.
Current classification of zirconia in dentistry: an updated review

Zirconia, a crystalline oxide of zirconium, holds good mechanical, optical, and biological properties. The metal-free restorations, mostly consisting of all-ceramic/zirconia restorations, are becoming popular restorative materials in restorative and prosthetic dentistry choices for aesthetic and biological reasons. Dental zirconia has increased over the past years producing wide varieties of zirconia for prosthetic restorations in dentistry. At present, literature is lacking on the recent zirconia biomaterials in dentistry. Currently, no article has the latest information on the various zirconia biomaterials in dentistry. Hence, the aim of this article is to present an overview of recent dental zirconia biomaterials and tends to classify the recent zirconia biomaterials in dentistry. This article is useful for dentists, dental technicians, prosthodontists, academicians, and researchers in the field of dental zirconia.
Technical Note: Monitoring discharge of mountain streams by 1 retrieving image features with deep learning

6 Traditional discharge monitoring usually relies on measuring flow velocity and cross- 7 section area with various velocimeters or remote-sensing approaches. However, the 8 topography of mountain streams in remote sites largely hinders the applicability of 9 velocity-area methods. We here present a method to continuously monitor mountain 10 stream discharge using a low-cost commercial camera and deep learning algorithm. A 11 procedure of automated image categorization and discharge classification was 12 developed to extract information on flow patterns and volumes from high-frequency 13 red–green–blue (RGB) images with deep convolutional neural networks (CNNs). The 14 method was tested at a small, steep, natural stream reach in southern China. Reference 15 discharge data was acquired from a V-shaped weir and ultrasonic flowmeter installed a 16 few meters downstream of the camera system. Results show that the discharge-relevant 17 stream features implicitly embedded in RGB information can be effectively recognized 18 and retrieved by CNN to achieve satisfactory accuracy in discharge measurement. 19 Coupling CNN and traditional machine learning models (e.g., support vector machine 20 and random forest) can potentially synthesize individual models’ diverse merits and 21 improve generalization performance. Besides, proper image pre-processing and 22 categorization are critical for enhancing the robustness and applicability of the method 23 under environmental disturbances (e.g., weather and vegetation on river banks). Our 24 study highlights the usefulness of deep learning in analyzing complex flow images and 25 tracking flow changes over time, which provides a reliable and flexible alternative
Detecting the First Quasars with ALMA

We show that ALMA is the first telescope that can probe the dust-obscured central region of quasars at z > 5 with a maximum resolution of ~ 30 pc employing the 18 km baseline. We explore the possibility of detecting the first quasars with ALMA (Schleicher, Spaans, & Klessen 2009). For this purpose, we adopt the Seyfert 2 galaxy NGC 1068 as a reference system and calculate the expected fluxes if this galaxy were placed at high redshift. This choice is motivated by the detailed observations available for this system and the absence of any indication for an evolution in metallicity in high-redshift quasars. It is a conservative choice due to the moderate column densities in NGC 1068, leading to moderate fluxes.
Task-Oriented Multimodal Communication Based on Cloud–Edge–UAV Collaboration

Cloud–edge–unmanned aerial vehicle (UAV) computing collaboration is crucial for smart and real-time applications. It highlights the challenges faced by UAVs in task execution, including limited endurance, computing, and communication capabilities, as well as complex external interference. This article proposes a task-driven multimodal communication technology that utilizes a multimodal confusion information reception model to improve the UAV’s ability to demodulate multimodal information. A cloud–edge–UAV collaborative computing model is then proposed to integrate cloud and edge computing capabilities into the UAV task execution system. As a result, the success probability and multimodal diversity of UAV task execution can be improved in complex environments based on both multimodal communication and cloud–edge–UAV collaboration concepts. Numerical results show that: 1) for each single-modal reception, the success probability increases by 15%, and the task execution is robust in severe environmental conditions since the success probability of eavesdropping by malicious users is reduced by 88%; 2) after multimodal combination, additional multimodal receptions results in an increase of the success probability; and 3) the reliability is highly related with the number of independent modals, which equals the multimodal diversity.
þ-Forking and Stable Forking

We use the construction of an independence relation to prove that þ-forking is equivalent to forking of a stable formula (in the sense of st-forking, see Definition 1.3) in all theories, and to show that from þ-dividing one can always achieve strong dividing over a base which is in the definable closure of the parameter set. © 2016. Acad. Colomb. Cienc. Ex. Fis. Nat. All rights reserved.
Characterization of Bundaberg Bee Virus 4 Genome Identified in Hawaii

To identify genetic markers of different cultivars of Stevia rebaudiana (stevia), leaf samples from twenty-three genetically unique stevia plants were subjected to RNA sequencing. Unexpectedly, transcriptome annotation revealed a ‘viral polyprotein’ contig, which was identified as the Bundaberg Bee Virus 4 (BBV4), a virus that has not been found in Hawaii before. BBV4 expression was found in twelve samples, and the expression levels ranged from 0.08-339.18 transcripts per million (TPM). A ‘BBV4-Hawaii’ consensus sequence was generated from 227,427 reads and shares 99.7% identity with the BBV4 reference sample from Australia. A phylogenetic tree estimation constructed using three viral domains placed BBV4 in a monophyletic clade with other members of Iflaviridae, supporting an Iflaviridae classification. A BBV4 intergenic region (IGR) was found to contain a ‘slippery sequence’ within a region that is predicted to contain the unique structure required for Programmed Ribosomal Shifting (PRS). A homological comparison of the BBV4 IGR to two other Picornavirales that utilize alternative translation revealed an area of overlap in the analogous regions. These results suggest that BBV4 may utilize alternative translation and that its IGR is involved in the process. This study also demonstrates the utility of repurposing large Next Generation Sequence (NGS) datasets with sufficiently deep coverage for viral genome identification, quantification, and characterization. The results herein represent the first full capture of the BBV4 genomic sequence and the second reported observation of the virus worldwide.
A New Class of Filtering Coupler Networks in Both Co-Directional and Contra-Directional Configurations Based on Cross-Coupled Resonators

This article presents a comprehensive treatment and a systematic design methodology of filtering couplers based on canonical coupled resonator networks. A theoretical framework for the network design process is first presented, paving the way to a straightforward design procedure for couplers with three types of directional characteristics: quadrature forward couplers, quadrature backward couplers, and backward 180° rat-race couplers. The design aspects of each type are thoroughly discussed, and four design examples are provided to detail the design process. Two different realizations in ridge waveguides are provided as proofs of concepts. However, any of the available technologies can be utilized to realize the coupler networks. The two prototypes were designed, simulated, fabricated, and tested demonstrating measured results that agree with theoretical predictions. These results show the potential of the proposed approach for synthesizing filtering couplers with a canonical topology suitable for higher order filtering couplers.
Crosstalk between Breast Milk N-Acetylneuraminic Acid and Infant Growth in a Gut Microbiota-Dependent Manner

The healthy growth of infants during early life is associated with lifelong consequences. Breastfeeding has positive impacts on reducing obesity risk, which is likely due to the varied components of breast milk, such as N-acetylneuraminic acid (Neu5Ac). However, the effect of breast milk Neu5Ac on infant growth has not been well studied. In this study, targeted metabolomic and metagenomic analyses were performed to illustrate the association between breast milk Neu5Ac and infant growth. Results demonstrated that Neu5Ac was significantly abundant in breast milk from infants with low obesity risk in two independent Chinese cohorts. Neu5Ac from breast milk altered infant gut microbiota and bile acid metabolism, resulting in a distinct fecal bile acid profile in the high-Neu5Ac group, which was characterized by reduced levels of primary bile acids and elevated levels of secondary bile acids. Taurodeoxycholic acid 3-sulfate and taurochenodeoxycholic acid 3-sulfate were correlated with high breast milk Neu5Ac and low obesity risk in infants, and their associations with healthy growth were reproduced in mice colonized with infant-derived microbiota. Parabacteroides might be linked to bile acid metabolism and act as a mediator between Neu5Ac and infant growth. These results showed the gut microbiota-dependent crosstalk between breast milk Neu5Ac and infant growth.