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SignificanceThe ability to detect and localize specific molecules through tissue is important for elucidating the molecular basis of disease and treatment. Unfortunately, most current molecular imaging tools in tissue either lack high spatial resolution (e.g., diffuse optical fluorescence tomography or positron emission tomography) or lack molecular sensitivity (e.g., micro-computed tomography, μCT). X-ray luminescence imaging emerged about 10 years ago to address this issue by combining the molecular sensitivity of optical probes with the high spatial resolution of x-ray imaging through tissue. In particular, x-ray luminescence computed tomography (XLCT) has been demonstrated as a powerful technique for the high-resolution imaging of deeply embedded contrast agents in three dimensions (3D) for small-animal imaging.AimTo facilitate the translation of XLCT for small-animal imaging, we have designed and built a small-animal dedicated focused x-ray luminescence tomography (FXLT) scanner with a μCT scanner, synthesized bright and biocompatible nanophosphors as contrast agents, and have developed a deep-learning-based reconstruction algorithm.ApproachThe proposed FXLT imaging system was designed using computer-aided design software and built according to specifications. NaGdF4 nanophosphors doped with europium or terbium were synthesized with a silica shell for increased biocompatibility and functionalized with biotin. A deep-learning-based XLCT image reconstruction was also developed based on the residual neural network as a data synthesis method of projection views from few-view data to enhance the reconstructed image quality.ResultsWe have built the FXLT scanner for small-animal imaging based on a rotational gantry. With all major imaging components mounted, the motor controlling the gantry can be used to rotate the system with a high accuracy. The synthesized nanophosphors displayed distinct x-ray luminescence emission, which enables multi-color imaging, and has successfully been bound to streptavidin-coated substrates. Lastly, numerical simulations using the proposed deep-learning-based reconstruction algorithm has demonstrated a clear enhancement in the reconstructed image quality.ConclusionsThe designed FXLT scanner, synthesized nanophosphors, and deep-learning-based reconstruction algorithm show great potential for the high-resolution molecular imaging of small animals.

The Corona Virus Disease (COVID-19) has a huge impact on all of humanity, and people's disregard for COVID-19 regulations has sped up the disease's spread. Our study uses a state-of-the-art object detection model like YOLOv4 (You Only Look Once, version 4), a very effective tool, on real-time 25fps, 1920 X 1080 video data streamed live by a camera-mounted Unmanned Aerial Vehicle (UAV) quad-copter to observe proper maintenance of social distance in an area of 35m range in this study. The model has demonstrated remarkable efficacy in identifying and quantifying instances of social distancing, with an accuracy of 82% and little latency. It has been able to work efficiently with real-time streaming at 25–30 ms. Our model is based on CSPDarkNet-53, which was trained on the MS COCO dataset for image classification. It includes additional layers to capture feature maps from different phases. Additionally, the model's neck is made up of PANet, which is used to aggregate the parameters from various CSPDarkNet-53 layers. The CSPDarkNet-53's 53 convolutional layers are followed by 53 more layers in the model head, for a total of 106 completely convolutional layers in the design. This architecture is further integrated with YOLOv3, resulting in the YOLOv4 model, which will be used by our detection model. Furthermore, to differentiate humans The aforementioned method was used to evaluate drone footage and count social distance violations in real time. Our findings show that our model was reliable and successful at detecting social distance violations in real-time with an average accuracy of 82%. [ABSTRACT FROM AUTHOR]

BackgroundSalmonella enterica serotype Typhi is the cause of typhoid fever. It is a human-restricted pathogen, and few data exist on S. Typhi gene expression in humans.Methodology/principal findingsWe applied an RNA capture and amplification technique, Selective Capture of Transcribed Sequences (SCOTS), and microarray hybridization to identify S. Typhi transcripts expressed in the blood of five humans infected with S. Typhi in Bangladesh. In total, we detected the expression of mRNAs for 2,046 S. Typhi genes (44% of the S. Typhi genome) in human blood; expression of 912 genes was detected in all 5 patients, and expression of 1,100 genes was detected in 4 or more patients. Identified transcripts were associated with the virulence-associated PhoP regulon, Salmonella pathogenicity islands, the use of alternative carbon and energy sources, synthesis and transport of iron, thiamine, and biotin, and resistance to antimicrobial peptides and oxidative stress. The most highly represented group were genes currently annotated as encoding proteins designated as hypothetical, unknown, or unclassified. Of the 2,046 detected transcripts, 1,320 (29% of the S. Typhi genome) had significantly different levels of detection in human blood compared to in vitro cultures; detection of 141 transcripts was significantly different in all 5 patients, and detection of 331 transcripts varied in at least 4 patients. These mRNAs encode proteins of unknown function, those involved in energy metabolism, transport and binding, cell envelope, cellular processes, and pathogenesis. We confirmed increased expression of a subset of identified mRNAs by quantitative-PCR.Conclusions/significanceWe report the first characterization of bacterial transcriptional profiles in the blood of patients with typhoid fever. S. Typhi is an important global pathogen whose restricted host range has greatly inhibited laboratory studies. Our results suggest that S. Typhi uses a largely uncharacterized genetic repertoire to survive within cells and utilize alternate energy sources during infection.

An artificial intelligence-based system was developed to efficiently predict settling velocity (SV) using a large dataset comprised of 2726 samples. The ranges of particle size and fluid viscosity were 0.212 − 98.59 mm and 0.02 − 92800 mPa.s, respectively. Properties of particle and fluid were fed to a model as the inputs to obtain SV as the output. Six machine learning algorithms were tested for the prediction. The random forest (RF) performed better than other algorithms with a coefficient of determination of 0.98 and a mean square error of 0.0027. A simple decision support system was developed using the RF model. The current study demonstrates the complete methodology of modeling SV with ML. [ABSTRACT FROM AUTHOR]