Miracle-dz/UniFAR-FTUs huggingface dataset:
Antimicrobial resistance landscape in a metropolitan city context using open drain wastewater-based metagenomic analysis

The One Health concept recognizes the inextricable interactions of the diverse ecosystems and their subsequent effect on human, animal and plant health. Antimicrobial resistance (AMR) is a major One Health concern and is predicted to cause catastrophes if appropriate measures are not implemented. In this study, to understand the AMR landscape in metropolitan city context, we performed metagenomic analysis of open drain wastewater samples. We analysed 17 samples from open drains that receive influx from human, animal, agricultural and industrial wastes. Our data suggests that macrolide antibiotics have developed the highest resistance in the city through mutations in the 23S rRNA gene, which is present in multiple pathogens including Escherichia coli, Campylobacter jejuni, Acinetobacter baumannii, Streptococcus pneumoniae, Pseudomonas aeruginosa, Neisseria gonorrhoeae, Klebsiella pneumoniae and Helicobacter pylori . Except for a few geographical locations, most other locations show a similar landscape for AMR. Considering human mobility and other similar anthropogenic activities, we suggest that such an AMR landscape may be common across other regions.
Prediction of Metastatic Site Based On Somatic Gene Mutations in Primary Tumors in Prostate Cancer

ABSTRACT Objective: The goal of this research is to predict the most likely metastatic site(s) of a primary prostate cancer tumor that has been resected via radical prostatectomy; its genome has been sequenced to obtain a list of gene mutations; and after initial inspection of pelvic lymph nodes, there is no clinical evidence of metastasis. However, micrometastases might already be growing in distant organs and cannot be detected at the time of surgery. Background: The most common metastatic targets in prostate cancer (PCa) are the pelvic lymph nodes (PLN) and bones. The PLNs are routinely dissected by a procedure called pelvic lymph node dissection (PLND) simultaneously with the surgical removal of the prostate to detect the presence of metastatic growths. Additionally, the prostate-specific antigen (PSA) level is used to assess the existence of a metastatic stage. However, micrometastases in other organs and tissues might be overlooked. Methods: We downloaded publicly available prostate cancer tumor data from the website www.CbioPortal. org. After choosing the 25 most commonly mutated genes by metastatic site (MS) and finding genes that are uniquely mutated on specific metastatic sites, we found that the mutational signature of a prostate cancer tumor is associated with its MS, and thus, we developed a method to numerically predict this association. Results: After executing a computational algorithm on the data set of metastatic prostate tumors, it was found that we can predict metastatic sites with the following accuracies: bone (90.9%), retroperitoneum (87.5%), liver (83.0%), kidney (80.0%), pancreas (80.0%), adrenal glands (75.0%), lung (71.1%), and brain (72.5%). Conclusions: We successfully developed a method and an algorithm that predict the most likely metastatic site of a primary prostate cancer tumor based on its genetic mutations. The accuracy of the predictions for eight metastatic sites ranges from 71.1% to 90.9%, with an average of 80.5%.
A review of research on multimodal knowledge graphs in agriculture

In recent years, knowledge graphs have made great progress in the field of agriculture. However, the traditional knowledge graph construction is mainly based on the single modality of text, but in the actual production of agriculture, it is often difficult to accurately describe the real data scene by using the single-modal data to construct knowledge graphs, thus increasing the limitations of the use of knowledge graphs. The article provides a detailed introduction to the basic concepts of multimodal knowledge graphs and the core technologies. For the application of multimodal knowledge graph in agriculture, the article focuses on summarizing the research on the application of knowledge graph in agricultural intelligent question and answer, crop monitoring, agricultural product recommendation and other fields in agriculture. It also looks forward to and analyses the challenges of constructing multimodal knowledge graph in agriculture and the prospect of multimodal knowledge graph in the field of agriculture.
Dynamic safety assessment of airborne based on Beidou rescue

The Radio-Determination Satellite Service (RDSS) is a unique and advantageous service of the Beidou system, and aircraft equipped with Beidou RDSS can be widely used in rescue sites. This paper evaluates the security of ground 2.5GHz 5G base stations and aircraft with Beidou RDSS systems. In the study, the dynamic simulation results of the impact of different flight altitudes and horizontal isolation distances on the system performance in urban and rural nonline of sight scenarios are given through aggregate interference simulations. The analysis results show that the higher the flight altitude of the aircraft, the easier it is for the two systems to coexist, and the rural scenario is easier to coexist than the urban scenario. Finally, the conditions for the safe operation of the 2.5GHZ 5G system and the RDSS aircraft are derived to provide a reference for practical situations.
Quantum correlations for hiding images in noise

A limitation of free-space optical communications is the ease with which the information can be intercepted. Overcoming this limitation is possible by hiding the information within the background optical noise that is present in all real-world situations. We demonstrate the limitations of our experimental system for transferring images over free-space using a photon-pair source emitting two correlated beams. The system uses spontaneous parametric down-conversion to create photon-pairs, where one photon contains the spatial information and the other the heralding information.
Fast and robust Fourier ptychographic microscopy with position misalignment correction

Abstract. Significance Fourier ptychographic microscopy (FPM) is a new, developing computational imaging technology. It can realize the quantitative phase imaging of a wide field of view and high-resolution (HR) simultaneously by means of multi-angle illumination via a light emitting diode (LED) array, combined with a phase recovery algorithm and the synthetic aperture principle. However, in the FPM reconstruction process, LED position misalignment affects the quality of the reconstructed image, and the reconstruction efficiency of the existing LED position correction algorithms needs to be improved. Aim This study aims to improve the FPM correction method based on simulated annealing (SA) and proposes a position misalignment correction method (AA-C algorithm) using an improved phase recovery strategy. Approach The spectrum function update strategy was optimized by adding an adaptive control factor, and the reconstruction efficiency of the algorithm was improved. Results The experimental results show that the proposed method is effective and robust for position misalignment correction of LED arrays in FPM, and the convergence speed can be improved by 21.2% and 54.9% compared with SC-FPM and PC-FPM, respectively. Conclusions These results can reduce the requirement of the FPM system for LED array accuracy and improve robustness.
Machine-learning-based microwave ghost imaging using reconfigurable metasurface

This paper presents a machine-learning based approach to microwave ghost imaging. Unlike traditional imaging techniques, ghost imaging is a non-local imaging technique that does not involve scanning. Since ghost imaging uses a single detector at a fixed location, measurements are taken under various random or pseudo-random illuminations to recover the image. Moving from the optical frequency regime to the microwave regime, the accurate measurement of such a random field becomes more complicated because there is no device such as a Charge Coupled Device (CCD) camera at microwaves. In this work, we generate pseudo-random field patterns using a reconfigurable metasurface and we use a single detector at a fixed location to measure the reflection from the target. Reconfigurability plays a fundamental role in ghost imaging. The microstrip line-fed metasurface is formed by unit cells of size 2.564 × 1 mm2, which are tuned by a pair of PIN diodes. By activating these diodes in forward or reverse bias, different field patterns can be generated. A receiver is placed in the centre of the aperture. The resonance frequency is set to 40 GHz. To recover the image, we present a machine-learning approach. A neural network is trained using a synthetic dataset obtained from ANSYS High Frequency Structure Simulator (HFSS) simulations to learn the second-order correlation between the incident field patterns and the measured scattered fields at the detector in order to recover the target image.
Shear Stiffness And Maximum Shear Stress of Tubular Members

Table 1. Traditional shear formulae for several cross-sections Cross-Section Shear Stiffness s GA Maximum Shear Stress max τ 5 6 GA 3 2 V A 32 37 GA 3 V A 1 2 GA A V 2 web GA 15 14 web V A b h 2 2 2 10 12 h GA h b + 2 3 4 2 4 b V A h + The shear stiffness and the maximum shear stress predicted by commonly used formulae are accurate for thin-wall tubes but too small for thick-wall tubes. New formulae for the shear stiffness and the maximum shear stress are proposed.
Analysis of airborne formation combat intent based on attention-based multi-level LSTM

Air combat target tactical intent refers to the analysis and inference of the enemy's combat intentions in real-time, adversarial environments by extracting battlefield environmental information, static attributes, and real-time dynamic information of air combat targets, combined with knowledge from the military domain. To achieve this goal, many machine learning-based methods have been proposed to infer aircraft intentions. However, these methods are only applicable to individual aircraft and cannot predict the intentions of the entire formation. Therefore, we propose an attention-based multi-level LSTM model that incorporates multiple levels and attention mechanisms to enhance the focus on key information and improve prediction efficiency, resulting in promising experimental results.
Single-line LiDAR indoor SLAM based on image interpretation

Simultaneous Localization and Mapping (SLAM) is a research hotspot with increasing demand for indoor navigation and mapping. Single-line LiDAR is an effective sensor for indoor SLAM due to its low cost and high accuracy. However, its limited field of view makes it difficult to obtain comprehensive 3D point clouds. In this paper, we propose a single-line LiDAR indoor SLAM based on image interpretation. Our proposed system utilizes the Cartographer framework as its backbone and mainly consists of four components, including input sensor data, sematic detection, local SLAM and global SLAM. This approach effectively addresses the limitations of traditional single-line LiDAR SLAM methods that do not incorporate image interpretation and Cartographer map. Experimental results show that our proposed method achieves accurate and robust localization and mapping in indoor environments, and outperforms traditional single-line LiDAR SLAM methods without image interpretation.
A design to realize power loss data protection based on non-volatile DIMM in domestic platform

With the development of information technology, storage-computing devices are widely used in the information field. Most of storage-computing devices usually have to maintain their storage and computing performance by introducing data cache technology, such compromise put the storage-computing devices in the dilemma of being challenged by the risk of losing their cache data when the power loses accidentally. In order to prevent the cache data loss caused by the accidental power loss of the system, this paper designs a method to realize power loss data protection based on Non-volatile DIMM in domestic platform, Specifically, install the Non-volatile DIMM memory in the storage-computing devices, the BMC is used to trigger the curing preservation operation of the memory in the case of power loss . When the system is suddenly power off, under the endurance of the super-capacitor, the processing unit in non-volatile memory migrates the data in DRAM to NAND Flash for curing storage, this ensures that data will not lost even in the event of a sudden power loss ,achieving the purpose of memory data non-volatile, ensuring the data security of the device.
Machine learning-based quality prediction for laser cleaning of composite paint layers

In order to predict the laser cleaning quality of composite paint layer and realize the controllable paint removal of composite paint layer, this paper proposes a machine-learning based laser cleaning quality prediction method for composite paint layer. The aluminum alloy substrate surface is uniformly coated with 20 μm green epoxy primer and 43μmwhite polyurethane topcoat as experimental samples for laser cleaning experiments; based on the experimental data combined with the three machine learning algorithms (support vector machine SVR, BP neural network, and random forest RF) to establish a prediction model between the process parameters and the cleaning quality. The experimental results show that, compared with the RF model and BP neural network model, the SVR model is more accurate in predicting the quality of laser cleaning of composite paint layers, and the coefficient of determination of the prediction model is 0.961, the root mean square error is 1.738, and the average absolute error is 1.5162. This study obtains the prediction model of the quality of removing the paint thickness with high accuracy, realizes the effective prediction of the quality of the laser removing the paint for the It lays a model foundation for further research on intelligent control of laser cleaning of composite paint layers.
Identification of a targetable ST2-expressing fibroblast subset driving Peutz-Jeghers syndrome polyposis

Peutz-Jeghers syndrome (PJS) is associated with early-onset and recurring gastrointestinal hamartomatous polyposis caused by hereditary inactivating mutations in the tumor suppressor gene LKB1 (STK11). Due to lack of efficient prophylactic therapies PJS patients require regular surgical interventions and have an increased risk of cancer. LKB1-deficient fibroblasts have been identified as drivers of polyposis, but a safely druggable target remains to be identified. Here we investigate tumorigenic mechanisms in PJS polyps using single-cell RNA sequencing of predisposed and tumor tissue and identified a ST2-expressing crypt top fibroblast (CTF) cluster enriched in polyps. The relevance of CTFs was supported by the fast polyposis following deletion of Lkb1 in CTFs. The transcriptional signature characterizing the ST2+ fibroblasts (ST2-CTFs) was enriched in inflammation-associated fibroblasts, and PJS polyposis was exacerbated by inflammation. Cell-cell communication analysis identified the ST2-CTF signature gene interleukin 11 (IL-11) as an upstream regulator of the ST2-CTFs, and consistently, reprogramming toward ST2 fibroblasts in vitro was dependent on IL-11. Importantly, a neutralizing IL-11 antibody efficiently reduced the tumor burden in a PJS mouse model. In summary, our results reveal ST2+ tumorigenic fibroblasts as drivers of PJS polyposis and identify IL-11 as the key mediator and a potential therapeutic target in PJS.
A multi-strategy antimicrobial discovery approach reveals new ways to combat Chlamydia

While the excessive use of broad-spectrum antibiotics is a major driver of the global antibiotic resistance crisis, more selective therapies remain unavailable for the majority of bacterial pathogens. This includes Chlamydia spp. , which cause millions of urogenital, ocular, and respiratory infections each year. We here report major conceptual additions to the available toolkit for antichlamydial discovery, including both experimental and computational approaches. Moreover, we report the to date most comprehensive search of the chemical space for novel antichlamydial activities, which identified over sixty compounds that are chemically diverse, structurally different from known antibiotics, non-toxic to human cells, and highly potent in blocking Chlamydia growth. While some compounds caused a reversible block in Chlamydia development, others could eradicate both established and persistent infections in a bactericidal manner. The most potent antichlamydials displayed compelling selectivity, some also synergies with clinically used antibiotics, as well as interactions profiles enabling predictions of molecular modes of action. Moreover, one compound displayed reduced antichlamydial efficacy in autophagy-deficient cells, suggesting a host- targeted activity. Altogether, we suggest that these novel antichlamydials could serve as tools for advancing our understanding of Chlamydia biology and as chemical starting points for developing more sustainable therapeutics for one of the most successful groups of intracellular pathogens.
Phosphogluconolactonase as the linchpin of an efficient pentose phosphate pathway

The metabolic networks of microorganisms are remarkably robust to genetic and environmental perturbations. This robustness stems from redundancies such as gene duplications, isoenzymes, alternative metabolic pathways, and also from non-enzymatic reactions. In the oxidative branch of the pentose-phosphate pathway (oxPPP), 6-phosphogluconolactone hydrolysis into 6-phosphogluconate is catalysed by 6-phosphogluconolactonase (Pgl) but in the absence of the latter, the oxPPP flux is thought to be maintained by spontaneous hydrolysis. However, in 11pgl Escherichia coli, an extracellular pathway can also contribute to pentose-phosphate synthesis. This raises question as to whether the non-enzymatic reaction can compensate for the absence of 6-phosphogluconolactonase and, ultimately, on the role of 6-phosphogluconolactonase in central metabolism. Our results indicate that in the absence of Pgl, this bypass pathway accounts for the entire flux into the oxPPP, suggesting that non-enzymatic hydrolysis does not compensate for the absence of Pgl and demonstrating that Pgl is critical for an efficiently functioning oxPPP.
HUNGER DRIVES SWITCHING AND SEARCHING RESPONSE IN A SOCIAL PREDATOR

Hunger is a frequent state for many predators and increasing hunger is likely to motivate costly behaviour to acquire necessary resources. Generalist predators must balance the costs and gains of hunting different prey, including increasing encounter rates and improving success rates by seeking areas with greater prey catchability. Large carnivores face threats when they interact with humans or conspecifics. We use integrated step selection analysis to describe spatiotemporal factors that influence wolf (Canis lupus) hunting behavior in Riding Mountain National Park, a natural area that wolves share with moose (Alces alces) and elk (Cervus canadensis). If hunger generates more risky behavior by wolves, as time-from-kill increases we expect wolves will: (1) search for and kill a prey that poses higher risk of injury, (2) use the periphery of their range, (3) use areas closer to the park boundary. Hunger alters wolf space use and drives a fine scale change in prey tracking. Movement patterns of hungry wolves are indicative of search behavior, i.e., shorter steps and more turning. Contrary to our predictions, hungry wolves moved further into the park. As wolves become hungrier, they switch their response from a weak selection to avoidance of elk. In contrast, the response to the primary and emergent prey, moose varied between individuals with some pack level similarities. Therefore, the state-based response to a pervasive risk and a historical resource was conserved in a population residing in a prey rich ‘island’ interfacing with human disturbance.
Quantifying the impact of genotype-dependent gene flow on mutation fixation in subdivided populations

In the wild, any population is likely to be spatially structured. Whereas we deeply understand evolutionary dynamics in well-mixed populations, our understanding of evolutionary dynamics in subdivided populations needs to be improved. In this work, I quantify the impact of genotype-dependent gene flow on the evolutionary dynamics of a subdivided population. Specifically, I build a model of a population structured as the island or the stepping stone model in which genotype-dependent gene flow is represented by individuals migrating between its sub-populations at a rate depending on their genotype. I analytically calculate the fixation probability and time of a mutation arising in the subdivided population under the low migration limit, which I validate with numerical simulations. I find that the island and the stepping stone models lead to the same fixation probability. Moreover, comparing the fixation probability in these models to the one in a well-mixed population of the same total census size allows me to identify an effective selection coefficient and population size. In the island and the stepping stone models, the effective selection coefficient differs from the selection coefficient if the wild-type and the mutant migration rates are different, whereas the effective population size equals the total census size. Finally, I show that genotype-dependent gene flow increases the fixation time, which allows for distinguishing the island and the stepping stone models, as opposed to the fixation probability.
Filter-aided expansion proteomics

Integration of hydrogel-based tissue expansion and mass spectrometry-based proteomics enables high spatial resolution analysis of global protein expression in tissues. Here, we introduce a filter-aided tissue expansion proteomics (FAXP) method with optimized hydrogel preparation and treatment steps for spatial proteomics analysis of formalin-fixed paraffin-embedded (FFPE) specimens. Compared with our original ProteomEx method, FAXP, partially automated with robotics, exhibited 14.5-fold higher volumetric resolution, increased peptide yield leading to 250% more protein identifications, and 50% time consumption for sample preparation. We demonstrated its application in clinical colorectal tissue samples. We further integrated it with laser capture microdissection for proteomic analysis of subcellular organelles.
Histone N-tails modulate sequence-specific positioning of nucleosomes

The precise mechanisms governing sequence-dependent positioning of nucleosomes on DNA remain unknown in detail. Existing algorithms, taking into account the sequence-dependent deformability of DNA and its interactions with the histone globular domains, predict rotational setting of only 65% of human nucleosomes mapped in vivo. To uncover novel factors responsible for the nucleosome positioning, we analyzed potential involvement of the histone N-tails in this process. To this aim, we reconstituted the H2A/H4 N-tailless nucleosomes on human BRCA1 DNA (∼100 kb) and compared their positions and sequences with those of the wild-type nucleosomes. In the case of H2A tailless nucleosomes, the AT content of DNA sequences is changed locally at superhelical location (SHL) ±4, while maintaining the same rotational setting as their wild-type counterparts. Conversely, the H4 tailless nucleosomes display widespread changes of the AT content near SHL ±1 and SHL ±2, where the H4 N-tails interact with DNA. Furthermore, a substantial number of H4 tailless nucleosomes exhibit rotational setting opposite to that of the wild-type nucleosomes. Thus, our findings strongly suggest that the histone N-tails are operative in selection of nucleosome positions, which may have wide-ranging implications for epigenetic modulation of chromatin states.