Miracle-dz/UniFAR-FTUs huggingface dataset:
Gut Microbiome–Dependent Metabolic Pathways and Risk of Lethal Prostate Cancer: Prospective Analysis of a PLCO Cancer Screening Trial Cohort

Background: Diet and the gut microbiome have a complex interaction that generates metabolites with an unclear effect on lethal prostate cancer risk. Identification of modifiable risk factors for lethal prostate cancer is challenging given the long natural history of this disease and difficulty of prospectively identifying lethal cancers. Methods: Mass spectrometry was performed on baseline serum samples collected from 173 lethal prostate cancer cases and 519 controls enrolled in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening trial. Baseline serum levels of choline, carnitine, betaine, γ-butyrobetaine, crotonobetaine, phenylacetylglutamine, hippuric acid, and p-cresol sulfate were quantified and analyzed by quartile. Conditional multivariable logistic regression analysis associated analyte levels with lethal prostate cancer incidence after adjusting for body mass index and PSA. The Cochran–Armitage test evaluated analyte level trends across quartiles. Results: Relative to those in the first quartile, cases with the highest baseline levels of choline (Q4 OR: 2.19; 95% CI, 1.23–3.90; P-trend: 0.005) and betaine (Q4 OR: 1.86; 95% CI, 1.05–3.30; P-trend: 0.11) exhibited increased odds of developing lethal prostate cancer. Higher baseline serum levels of phenylacetylglutamine (Q4 OR: 2.55; 95% CI, 1.40–4.64; P-trend: 0.003), a gut microbiome metabolite of phenylalanine with adrenergic activity, were also associated with lethal prostate cancer. Conclusions: Baseline serum levels of one-carbon methyl donors and adrenergic compounds resulting from human and gut microbiota–mediated metabolism are associated with increased lethal prostate cancer risk. Impact: Dietary composition, circulating metabolite levels, and downstream signaling pathways may represent modifiable risk factors associated with incident lethal prostate cancer. Beta-adrenergic blockade represents an additional target for oncologic risk reduction.
Gene targeting in polymerase theta‐deficient Arabidopsis thaliana

SUMMARY Agrobacterium tumefaciens‐mediated transformation has been for decades the preferred tool to generate transgenic plants. During this process, a T‐DNA carrying transgenes is transferred from the bacterium to plant cells, where it randomly integrates into the genome via polymerase theta (Polθ)‐mediated end joining (TMEJ). Targeting of the T‐DNA to a specific genomic locus via homologous recombination (HR) is also possible, but such gene targeting (GT) events occur at low frequency and are almost invariably accompanied by random integration events. An additional complexity is that the product of recombination between T‐DNA and target locus may not only map to the target locus (true GT), but also to random positions in the genome (ectopic GT). In this study, we have investigated how TMEJ functionality affects the biology of GT in plants, by using Arabidopsis thaliana mutated for the TEBICHI gene, which encodes for Polθ. Whereas in TMEJ‐proficient plants we predominantly found GT events accompanied by random T‐DNA integrations, GT events obtained in the teb mutant background lacked additional T‐DNA copies, corroborating the essential role of Polθ in T‐DNA integration. Polθ deficiency also prevented ectopic GT events, suggesting that the sequence of events leading up to this outcome requires TMEJ. Our findings provide insights that can be used for the development of strategies to obtain high‐quality GT events in crop plants.
Nanopore sensors for viral particle quantification: current progress and future prospects

ABSTRACT Rapid, inexpensive, and laboratory-free diagnostic of viral pathogens is highly critical in controlling viral pandemics. In recent years, nanopore-based sensors have been employed to detect, identify, and classify virus particles. By tracing ionic current containing target molecules across nano-scale pores, nanopore sensors can recognize the target molecules at the single-molecule level. In the case of viruses, they enable discrimination of individual viruses and obtaining important information on the physical and chemical properties of viral particles. Despite classical benchtop virus detection methods, such as amplification techniques (e.g., PCR) or immunological assays (e.g., ELISA), that are mainly laboratory-based, expensive and time-consuming, nanopore-based sensing methods can enable low-cost and real-time point-of-care (PoC) and point-of-need (PoN) monitoring of target viruses. This review discusses the limitations of classical virus detection methods in PoN virus monitoring and then provides a comprehensive overview of nanopore sensing technology and its emerging applications in quantifying virus particles and classifying virus sub-types. Afterward, it discusses the recent progress in the field of nanopore sensing, including integrating nanopore sensors with microfabrication technology, microfluidics and artificial intelligence, which have been demonstrated to be promising in developing the next generation of low-cost and portable biosensors for the sensitive recognition of viruses and emerging pathogens.
Effect of [Zr(α-PW11O39)2]10− Polyoxometalate on the Self-Assembly of Surfactant Molecules in Water Studied by Fluorescence and DOSY NMR Spectroscopy

The catalytic fragmentation of hydrophobic proteins by polyoxometalates (POMs) requires the presence of surfactants in order to increase the solubility of the protein. Depending on the nature of the surfactant, different effects on the kinetics of protein hydrolysis are observed. As the molecular interactions between the POMs and surfactants in solutions have been scarcely explored, in this study, the interaction between the catalytically active Keggin polyoxometalate [Zr(α-PW11O39)2]10− and four different surfactants—sodium dodecyl sulfate (SDS), dodecyldimethyl(3-sulfopropyl)ammonium (Zw3-12), dodecyldimethyl(3-sulfopropyl) ammonium (CHAPS), and polyethylene glycol tert-octylphenyl ether (TX-100)—have been studied in aqueous media. The effect of polyoxometalate on the self-assembly of surfactant molecules into micelles and on the critical micellar concentration (CMC) has been examined by fluorescence spectroscopy and diffusion ordered NMR spectroscopy (DOSY).
Waveform parameters of fields generated by lightning strokes to the CN tower and to objects in its vicinity

Since 1991, broadband, high-resolution measurement systems have simultaneously captured the lightning current derivative at the CN tower and the corresponding lightning-generated electric and magnetic fields 2 km north of the tower. Extensive cumulative statistics of waveform parameters (peak, 10% to 90% risetime to peak and pulse width at the 50% level of the peak) of electric and magnetic field signals, generated by lightning return-strokes to the CN Tower during the past eleven years (1992-2002), have been derived and are presented in this paper. Furthermore, waveform parameters of electric fields radiated from lightning strikes to the CN Tower and to other objects in its vicinity are compared. It is shown that the risk from electromagnetic interference (EMI) due to the lightning-generated electromagnetic pulse (LEMP) is substantially elevated near a tall structure. The presented statistical results will assist in the establishment of more sophisticated protective measures against interference due to lightning-generated electromagnetic pulses, especially those generated from lightning occurring at tall structures.
Screening and bioinformatics analysis of key biomarkers in acute myocardial infarction

Abstract Acute myocardial infarction (AMI) is the most severe manifestation of coronary artery disease. Considerable efforts have been made to elucidate its etiology and pathology, but the genetic factors that play a decisive role in the occurrence of AMI are still unclear. To determine the molecular mechanism of the occurrence and development of AMI, four microarray datasets, namely, GSE29111, GSE48060, GSE66360, and GSE97320, were downloaded from the Gene Expression Omnibus (GEO) database. We analyzed the four GEO datasets to obtain the differential expression genes (DEGs) of patients with AMI and patients with non-AMI and then performed gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Protein-protein interaction (PPI) network analysis. A total of 41 DEGs were identified, including 39 upregulated genes and 2 downregulated genes. The enriched functions and pathways of the DEGs included the inflammatory response, neutrophil chemotaxis, immune response, extracellular space, positive regulation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) transcription factor activity, response to lipopolysaccharide, receptor for advanced glycation end products (RAGE) receptor binding, innate immune response, defense response to bacterium, and receptor activity. The cytoHubba plug-in in Cytoscape was used to select the most significant hub gene from the PPI network. Ten hub genes were identified, and GO enrichment analysis revealed that these genes were mainly enriched in inflammatory response, neutrophil chemotaxis, immune response, RAGE receptor binding, and extracellular region. In conclusion, this study integrated four datasets and used bioinformatics methods to analyze the gene chips of AMI samples and control samples and identified DEGs that may be involved in the occurrence and development of AMI. The study provides reliable molecular biomarkers for AMI screening, diagnosis, and prognosis.
A New Phylogenetic Hypothesis for Cereinae (Cactaceae) Points to a Monophyletic Subtribe

Abstract Cereinae comprises 14 genera distributed in Neotropical dry forest formations such as in the Caatingas of northeastern Brazil or in rocky outcrops in the north of southeastern Brazil. Historically, the taxonomy of the group has been very controversial, especially regarding generic circumscriptions, and phylogenetic relationships within the group are still poorly understood. To investigate the delimitation of the subtribe and infra-subtribal relationships, we performed a phylogenetic analysis including 50 taxa representing 13 genera using one nuclear (PhyC) and four cpDNA (petL-psbE, trnL-trnT, trnS-trnG, and rpl16) regions. Our results show a monophyletic Cereinae with high support in Bayesian, maximum parsimony, and maximum likelihood analyses based on combined matrices. Although our results expand the knowledge of generic relationships, we emphasize the need for further molecular phylogenetic studies combined with ecological evidence to clarify relationships at the more inclusive nodes of the subtribe.
Comparison of two different techniques to determine the cloud cover from all-sky imagery

All sky-cameras are devices with a very high potential in order to study atmospheric phenomena and were originally designed to obtain the cloud cover. However, methods based in different approaches produce significant differences in the results. State-of-art methods usually offer better performance, thanks to computer vision and machine learning (ML) techniques, than traditional algorithms based on channel ratios using both fixed and adaptive thresholds to classify the pixels of one image as cloud or cloud free. We have developed a cloud cover adaptive threshold algorithm base on Probability Density Function (PDF) of the Blue to Red Ratio (BRR), standing out in: simplicity; ease of implementation; compatibility with any sky-camera in terms of technical requirement and type of image acquisition. The goal of this study is to compare our algorithm with a most fashionable method based on Machine learning, discovering the pros and cons of each one and weather ultimately less can be more. The comparison has been done using a set of 1-year HDR imagery database, representing a wider range of atmospheric scenarios such as clear sky, cloudy, partly cloudy and different types of aerosol conditions and clouds such as cirrus, cumulus, stratum and nimbus. To stablish a quantitative comparison of both methods, a limited set of images has been chosen. The PDF method show better agreement than our ML implementation, with a better performance for all weather conditions, in comparison against our cloud cover database.
Optimized MODIS channel selection to improve the NASA CERES FluxByCloudTyp product fluxes

The NASA Clouds and the Earth’s Radiant Energy System (CERES) project has provided the climate community more than a 20-year record of top of the atmosphere (TOA) reflected shortwave (SW) and emitted longwave (LW) fluxes. The fluxes are used to monitor the Earth’s energy balance as well as for climate model validation and cloud feedback studies. One of the largest uncertainties in climate models is the response of clouds in feedback studies. Reducing this uncertainty requires a more stringent validation of model-generated fluxes by cloud-type. Rather than relying on radiative transfer model generated fluxes from observed cloud-type retrievals, the CERES FluxByCloudTyp (FBCT) product relies on MODIS empirical narrowband to broadband relationships to convert the cloudy portions of the CERES footprints into fluxes. The overcast footprint and sub-footprint cloud layer fluxes are then stratified by 7 pressure layers and 6 optical depth bins and temporally averaged into daily and monthly regional cloud-type fluxes. The CERES project analyzed a total of 19 MODIS channels for use in the next generation FBCT product narrowband to broadband conversion. This was accomplished by comparing the narrowband to broadband RMS errors from all possible 5-channel combinations. The SW and LW optimized channels were selected by also considering analogous geostationary, MODIS and VIIRS imager channels as well as channel combinations that provided the lowest RMS errors across all surface types. The optimized channel combinations will be tested in the FBCT algorithm to investigate any narrowband to broadband dependencies as a function of cloud fraction, effective pressure, optical depth, PW, solar and view angle, surface type.
Weld bead extraction based on point cloud segmentation

At present, the welding seam extraction algorithm is for 2D space image processing, which has the disadvantages of complex extraction process, low accuracy and great limitations. Compared with the cumulative error caused by image mosaic, 3D can reflect the target features from various perspectives. At present, RANSAC fitting plane and Euclidean clustering extraction have been applied to 3D point cloud feature analysis, but RANSAC can only roughly fit the surface of the object, and it can not effectively extract features for the areas with rich surface curvature changes. Euclidean clustering is based on KD tree query where the algorithm needs to set the number of neighborhood points, so it has uncertainty. In order to overcome the above defects, this paper proposes a region growing segmentation algorithm, which can divide the region and extract the boundary according to the changes of the weld surface curvature and normal vector. At the same time, compared with the actual extraction results, the average error is reduced to about 1.0 mm. Compared with the traditional 2D laser line extraction, it can more intuitively reflect the weld features and put into practical production application.
Rock classification model based on transfer learning and convolutional neural network

∗ In geological work, some manual discrimination methods used for rock identification tasks are highly influenced by personal subjective factors and have low identification efficiency, and many experiments are performed on pre-processed rock thin sections, which are difficult to meet the requirements of modern geological research work. In order to solve the above problems, this paper proposes a rock classification model based on transfer learning and convolutional neural network using the original rock images as the data set, and finetune the VGG16 convolutional neural network frozen with three convolutional layers using the data expanded rock training set samples using transfer learning method. reached a maximum of 90.24%. The experimental results show that the rock classification model built by finetune and VGG16 in this paper has a short training period and can accurately classify and recognize rocks automatically.
Analysis on the evolution of Global trade space based on SNA

In the 21st century, the world has entered an era of open innovation characterized by the global flow of innovative factors. The primary trade market, which is mainly resource intensive and labor-intensive, has gradually shrunk, while the technology trade characterized by high added value has become one of the trade with the fastest growth in Global trade volume and the strongest development potential. Global technology trade will become the carrier of advanced technology and innovative technology in various countries and regions, and will launch fierce competition in the international market, promoting the evolution and reconstruction of global economy and science and technology system. With the rise of network science, complex network and social network analysis methods have been widely introduced into the field of international trade research. Global trade network research has become a hot spot and frontier, presenting the research context from network nature to spatial evolution simulation, from overall trade network to intra industry or intra product network, from formation mechanism to influencing factors. Therefore, based on the construction of technology trade network based on spatial connection, this paper attempts to analyze the topological elements such as the total scale of trade, network nodes and associations, and network communities, so as to explore the spatial characteristics of global technology trade network.
Research on Traffic Sign Detection and Recognition Algorithm Based on Convolutional Neural Network

∗ With the continuous progress of science and technology in China, unmanned driving technology has been continuously developed. The use of deep learning for traffic sign recognition has a strong capability of feature representation [1] , which is the most popular method at present. In this paper, the convolutional neural network algorithm is used to detect and classify traffic signs based on the German traffic sign data set, and the algorithm is verified experimentally. The algorithm preprocesses the data set by means of color image to grayscale image, histogram equalization and other meth-ods, and then continuously optimizes the neural network model. The experimental results show that the accuracy rate can reach 96.39%.
A cautionary tale on proper use of branch-site models to detect convergent positive selection

Comparative genomics has become a powerful tool to elucidate genotype-phenotype relationships, particularly through the study of convergently acquired phenotypes. By identifying genes under positive selection specifically on branches with the convergent phenotype we can potentially link genes to that phenotype. Such gene scans are often done using branch-site codon models. However, we have observed a recent troubling trend of misinterpretation of branch-site models in which phylogeny-wide positive selection is not distinguished from positive selection specific to convergent branches. Here, we use simulations and real data to demonstrate how failing to discern between these two cases leads to false inferences of positive selection associated with a convergent trait. We then present a “ drop-out” test solution to distinguish the two cases and thereby truly capture positive selection events associated with convergent phenotypes, thus allowing for further insights into both genetic and phenotypic evolution.
DeepMicrobeFinder Sorts Metagenomes into Prokaryotes, Eukaryotes and Viruses, with Marine Applications

Sequence classification is valuable for reducing the complexity of metagenomes and providing a fundamental understanding of the composition of metagenomic samples. Binary metagenomic classifiers offer an insufficient solution because metagenomes of most natural environments are typically derived from multiple sequence sources including prokaryotes, eukaryotes and the viruses of both. Here we introduce a deep-learning based (not reference-based) sequence classifier, DeepMicrobeFinder, that classifies metagenomic contigs into five sequence classes, e.g., viruses infecting prokaryotic or eukaryotic hosts, eukaryotic or prokaryotic chromosomes, and prokaryotic plasmids. At different sequence lengths, DeepMicrobeFinder achieved area under the receiver operating characteristic curve (AUC) scores >0.9 for most sequence classes, the exception being distinguishing prokaryotic chromosomes from plasmids. By benchmarking on 20 test datasets with variable sequence class composition, we showed that DeepMicrobeFinder obtained average accuracy scores of ~0.94, ~0.87, and ~0.92 for eukaryotic, plasmid and viral contig classification respectively, which were significantly higher than the other state-of-the-art individual predictors. Using a 1-300 µm daily time-series metagenomic dataset sampled from coastal Southern California as a case study, we showed that metagenomic read proportions recruited by eukaryotic contigs could be doubled with DeepMicrobeFinder’s classification compared to the counterparts of other reference-based classifiers. In addition, a positive correlation could be observed between eukaryotic read proportions and potential prokaryotic community growth rates, suggesting an enrichment of fast-growing copiotrophs with increased eukaryotic particles. With its inclusive modeling and unprecedented performance, we expect DeepMicrobeFinder will promote metagenomic studies of under-appreciated sequence types.
Analysis of the Influence of Residual Intensity Modulation in the Multifunction Integrated Optic Circuit on Fiber-Optic Gyroscopes Performance

The residual intensity modulation (RIM) of the multifunction integrated optic circuit (MIOC) can be classified into the linear term and the nonlinear term, which can impact the performance of the fiber-optic gyroscope (FOG). According to the generation mechanism of the RIM and the demodulation principle of the FOG, a model is established to analyze the influence of RIM on the FOG performance. Simulation shows that both the linear and the nonlinear RIM can lead to a large and sudden change in the FOG output at reset points of the feedback step wave, as well as fluctuations within the reset period. The magnitude of the sudden change at the reset point is proportional to the difference between the linear RIMs of two branches in the MIOC, and the fluctuation within the period of the feedback step wave is more dependent on the nonlinear RIM. The relationship between the FOG output and the linear and nonlinear RIMs are verified by experiments. The RIM-induced output can result in the increase of the bias error and the scale factor error of the FOG, especially at small input angular velocity. Furthermore, the application of FOG for the real-time high-speed position tracking to stabilize the attitude control platform can be limited.