Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanism of action of gut microbiota and probiotic Lactobacillus rhamnosus GG on skeletal remodeling in mice

Abstract Introduction Gut microbiota (GM) is the collection of small organisms such as bacteria, fungi, bacteriophages and protozoans living in the intestine in symbiotics relation within their host. GM regulates host metabolism by various mechanisms. Methods This review aims to consolidate current information for physicians on the effect of GM on bone health. For this, an online search of the literature was conducted using the keywords gut microbiota, bone mass, osteoporosis, Lactobacillus and sex steroid. Results and Conclusions There is a considerable degree of variation in bone mineral density (BMD) within populations, and it is estimated that a significant component of BMD variability is due to genetics. However, the remaining causes of bone mass variance within populations remain largely unknown. A well‐recognized cause of phenotypic variation in bone mass is the composition of the microbiome. Studies have shown that germ‐free (GF) mice have higher bone mass compared to conventionally raised (CR) mice. Furthermore, GM dysbiosis, also called dysbacteriosis, is defined as any alteration in the composition of the microbial community that has been colonized in the host intestine and associated with the development of bone diseases. For instance, postmenopausal osteoporosis (PMO) and diabetes. GM can be modulated by several factors such as genetics, age, drugs, food habits and probiotics. Probiotics are defined as viable bacteria that confer health benefits by modulating GM when administered in adequate quantity. Lactobacillus rhamnosus GG (LGG) is a great example of such a probiotic. LGG has been shown to regulate bone mass in healthy mice as well as ovariectomized (OVX) mice via two different mechanisms. This review will focus on the literature regarding the mechanism by which GM and probiotic LGG regulate bone mass in healthy mice as well as in OVX mice, a model of PMO.
Comparison of Baseline 68Ga-FAPI and 18F-FDG PET/CT for Prediction of Response and Clinical Outcome in Patients with Unresectable Hepatocellular Carcinoma Treated with PD-1 Inhibitor and Lenvatinib

Visual Abstract Fibroblast activation protein contributes to immunosuppression and resistance to immunotherapies. This study aimed to compare baseline 68Ga-labeled fibroblast activation protein inhibitor (68Ga-FAPI) PET/CT and 18F-FDG PET/CT in response and survival prediction in unresectable hepatocellular carcinoma (uHCC) patients treated with the combination of programmed cell death 1 (PD-1) inhibitor and lenvatinib. Methods: In this prospective cohort study, 22 patients with uHCC who underwent baseline 18F-FDG and 68Ga-FAPI PET/CT and soon began taking a combination of PD-1 inhibitor and lenvatinib were recruited. Semiquantitative indices of baseline PET/CT were measured as 18F-FDG SUVmax, metabolic tumor volume, total lesion glycolysis, 68Ga-FAPI SUVmax, 68Ga-FAPI–avid tumor volume (FTV), and total lesion fibroblast activation protein expression (TLF). The primary endpoint was durable or nondurable clinical benefit after treatment, and the secondary endpoints were progression-free survival (PFS) and overall survival (OS). Results: The overall response rate of the combination therapy was 41% (9/22). Fifty percent of patients had durable clinical benefit. Median PFS and OS were 4.8 and 14.4 mo, respectively. Patients with nondurable clinical benefit showed a significantly higher FTV and TLF than those with durable clinical benefit, whereas 18F-FDG parameters overlapped. A higher 68Ga-FAPI–avid tumor burden (FTV > 230.46 cm3 or TLF > 961.74 SUVbody weight⋅cm3) predicted both shorter PFS (4.0 vs. 13.5 mo, P = 0.016) and shorter OS (7.8 mo vs. not reached, P = 0.030). Patients with a higher metabolic tumor burden (metabolic tumor volume > 206.80 cm3 or total lesion glycolysis > 693.53 SUVbody weight⋅cm3) showed a shorter OS although the difference did not reach statistical significance (P = 0.085). In multivariate analysis, a higher 68Ga-FAPI–avid tumor burden (hazard ratio [HR], 3.88 [95% CI, 1.26–12.01]; P = 0.020) and macrovascular invasion (HR, 4.00 [95% CI, 1.06–15.14]; P = 0.039) independently predicted a shorter PFS, whereas a higher 68Ga-FAPI–avid tumor burden (HR, 5.92 [95% CI, 1.19–29.42]; P = 0.035) and bone metastases (HR, 5.88 [95% CI, 1.33–25.93]; P = 0.022) independently predicted a shorter OS. Conclusion: Volumetric indices on baseline 68Ga-FAPI PET/CT were potentially independent prognostic factors to predict durable clinical benefit, PFS, and OS in uHCC patients treated with a combination of PD-1 and lenvatinib. Baseline 68Ga-FAPI PET/CT may facilitate uHCC patient selection before combination therapy.
Bias corrected empirical Bayes confidence intervals for the selected mean

ABSTRACT Empirical Bayes (EB) methods are very useful for post selection inference. Following Datta et al. (2002), we construct EB confidence intervals for the selected population mean. The EB intervals are adjusted to achieve the target coverage probabilities asymptotically up to the second order. Both unconditional coverage probabilities of EB intervals and corresponding probabilities conditional on ancillary statistics are found.
Emerging disinfection by-products formation of various molecular weight organic matter fractions in raw water contaminated with treated wastewater

Abstract Combining dissolved organic matter (DOM) in raw water (RW) with DOM in treated wastewater (TWW) can react with chlorine and pose emerging disinfection by-products (DBPs). This study evaluated DOM based on the molecular weight (MW) size fractionation, trihalomethane, iodinated-trihalomethane, haloacetonitrile, and trichloronitromethane formation potential (THMFP, I-THMFP, HANFP, and TCNMFP) of the RW from the U-Tapao Canal, Songkhla, Thailand and the RW mixed with TWW (RW + TWW) samples. The RW and RW + TWW were treated by coagulation with poly aluminum chloride. The DOM of RW and RW + TWW and their treated water was distributed most in the MW below 1 kDa. The MWs of 3–10 kDa and 1–3 kDa were the active DOM involved in the specific THMFP for the RW + TWW. The MW of < 1 kDa in the RW + TWW resulted in a slightly high specific I-THMFP and HANFP. The MW of 1 − 3 kDa in the coagulated samples had a high specific I-THMFP. The MW of > 10 kDa in the coagulated RW + TWW was a precursor for a particular HANFP. Monitoring systems for measuring the level of TWW mixed with RW and an effective process to enhance the efficiency of traditional water treatment must be set up to produce a consumer-safe water supply.
Shared Three-Dimensional Robotic Arm Control Based on Asynchronous BCI and Computer Vision

Objective: A brain-computer interface (BCI) can be used to translate neuronal activity into commands to control external devices. However, using noninvasive BCI to control a robotic arm for movements in three-dimensional (3D) environments and accomplish complicated daily tasks, such as grasping and drinking, remains a challenge. Approach: In this study, a shared robotic arm control system based on hybrid asynchronous BCI and computer vision was presented. The BCI model, which combines steady-state visual evoked potentials (SSVEPs) and blink-related electrooculography (EOG) signals, allows users to freely choose from fifteen commands in an asynchronous mode corresponding to robot actions in a 3D workspace and reach targets with a wide movement range, while computer vision can identify objects and assist a robotic arm in completing more precise tasks, such as grasping a target automatically. Results: Ten subjects participated in the experiments and achieved an average accuracy of more than 92% and a high trajectory efficiency for robot movement. All subjects were able to perform the reach-grasp-drink tasks successfully using the proposed shared control method, with fewer error commands and shorter completion time than with direct BCI control. Significance: Our results demonstrated the feasibility and efficiency of generating practical multidimensional control of an intuitive robotic arm by merging hybrid asynchronous BCI and computer vision-based recognition.
A self-supervised learning approach for high-resolution diffuse optical tomography using neural fields

Diffuse optical tomography (DOT) has shown promise in biomedical research, such as breast cancer diagnostics and brain imaging, by reconstructing hidden objects within scattering media. However, the conventional reconstruction framework faces challenges due to the highly ill-posed inverse problem of reconstructing optical properties. This work introduces a novel approach, neural field-based diffuse optical tomography (NeuDOT), which leverages a multi-layer perceptron (MLP) to learn an implicit function that maps spatial coordinates to their corresponding optical absorption coefficients. The performance of the NeuDOT method has been evaluated through several phantom studies, demonstrating its potential for high spatial resolution DOT reconstruction
Three-dimensional temperature distribution mapping by generative adversarial network in low light environment using thermography

In this study, we propose a new framework to perform visual simultaneous localization and mapping (SLAM) with RGB images artificially generated from thermal images in low light environments where an optical camera cannot be applied. We applied contrastive unpaired translation (CUT) and enhanced generative adversarial network for super-resolution (ESRGAN), which are image translation methods to generate a clear realistic RGB image from a thermal image. Oriented FAST and rotated BRIEF (ORB)-SLAM was performed using the super-resolution fake RGB image to generate a 3D point cloud. Experimental results showed that our thermography-based visual SLAM could generate a 3D temperature distribution map in the low light environment.
A fast rerouting algorithm based on segment routing under single point failure

Traditional routing protection schemes usually adopt fast rerouting to deal with single-point failures in the network, which usually require a large amount of computation and communication, as well as specialized hardware or software, resulting in high latency and large overhead. In this paper, we propose a fast rerouting algorithm FRUSPOF based on SR technology for single-point failures. For possible failure nodes between each node on the shortest path tree and the root node, we construct segment label sequences to avoid the failure nodes. When there is no failure in the network, the shortest path is used for packet forwarding. When the network fails, the path calculated based on the SR sequence is used for packet forwarding. This algorithm can deal with single-point failures in the network and has a small additional overhead. We validate the effectiveness of the proposed fast rerouting method through three real networks in this paper.
Research on prediction of world buildings progress based on neural network algorithm

We screened the results for the most active countries in buildings research over the past decade. By screening the data, the BP neural network prediction model of countries with buildings was established 100 years later, and solved by matlab software, Iraq may have buildings in the next 100 years. finally, solve the relevant model of 2123 buildings of 10728 and made the number of buildings change trend chart.
Human motion recognition based on passive RFID and multi-model fusion

Aiming at the problems that the human motion recognition method based on machine vision and sensors is easy to reveal user privacy and inconvenient to carry, this paper proposes a human motion recognition method based on passive RFID and multi-model fusion. By wearing a flexible liquid metal tag on the human body, the effect of different actions on the RSSI signal of the receiver is utilized, The approximate component and detail component of the signal extracted by wavelet transform are used as fusion features to represent human motion, and the four models of KNN, DT, SVM and LR are fused to construct the Blending model with KNN, DT and SVM as primary learners and LR as secondary learners for motion recognition. The experiments show that the accuracy of the Blending model for the five movements of standing, sitting, walking, running and falling is 97.29%, Compared with individual learners, it has better recognition effect and greatly improves the convenience of human motion recognition.
The PCA feature extraction of target based on carrier-free UWB radar

Target recognition of obstacles in the highway is a challenge problem. Most of the abnormal events on the expressway are pigs, traffic cones, stones, etc. This question of radar target recognition includes: echo signals detection and echo signals processing. The echo signals of this paper were produced by impulse UWB radar. And the signals were processed with feature extraction by The PCA (Principal Component Analysis) machine learning algorithm. The PCA is an unsupervised method by calculating the covariance matrix of the data and performing eigenvalue decomposition on the covariance matrix. The engineering research in this paper shows that extracting “information principal components” of obstacles can greatly help to identify the target on the expressway. The research results can be widely used in time sensitive target recognition.
Lensed fiber-optic scanning two-photon endomicroscopic imaging

In recent years, two-photon endomicroscopy has developed as a promising label-free optical biopsy technique for diagnosing gastrointestinal tumors. In this study, we optimize the imaging resolution of the lensed fiber-optic scanning two-photon endomicroscopic imaging scheme. By fabricating a lensed fiber for fiber-optic scanning two-photon endomicroscopy, a lateral resolution of 2.1 μm and a field of view of 600 μm in two-photon endomicroscopic imaging is achieved. Furthermore, the objective-lens-free imaging capability is also validated using gastric
UAV trajectory design based on retransmission

Unmanned Aerial Vehicles (UAVs) have been widely used for data collection due to their high flexibility and ability to provide line-of-sight links. In existing studies, it is generally assumed that ground users can transmit only once, while in real systems, ground-based IoT devices can enhance the communication range by transmitting the same data multiple times. Therefore, this paper considers the UAV data collection problem in two different retransmission modes and models it as maximizing the number of users that can be served by the system for a determined amount of UAV onboard energy. Since the problem is a mixed integer nonlinear problem, it is difficult to solve directly. Therefore, it is decomposed into three subproblems by alternating optimization and solved by successive convex approximation (SCA). The simulation results show that the number of users that can be served by the system can be effectively increased by considering the retransmission of ground users.
Waveguide-integrated superconducting nanowire single-photon detectors

Abstract Integration of superconducting nanowire single-photon detectors with nanophotonic waveguides is a key technological step that enables a broad range of classical and quantum technologies on chip-scale platforms. The excellent detection efficiency, timing and noise performance of these detectors have sparked growing interest over the last decade and have found use in diverse applications. Almost 10 years after the first waveguide-coupled superconducting detectors were proposed, here, we review the performance metrics of these devices, compare both superconducting and dielectric waveguide material systems and present prominent emerging applications.
Performance comparison of division of time and division of focal plan polarimeters

Two typical instruments can be employed for linear polarization imaging: a rotating polarizer in front of a classical monochrome camera (division of time), or a dedicated sensor with a polarization filter array (division of focal-plane). The last method enables the snapshot acquisition of the linear polarization properties of the light with a compact and affordable instrument. The rotating polarizer method has until now been preferred when good polarimetric precision is required. It is still unclear how these two techniques perform comparatively in terms of polarimetric accuracy. This paper provides a practical comparison between the two methods, and evaluates the effect of pre-processing applied on raw images to counterbalance the differences.
A low complexity and high performance LDPC decoding algorithm

LDPC (Low Density Parity Check Codes) is a low -density puppet test code applied to channel codes. Among the existing LDPC decoding algorithms, the LLR-BP decoding arithmetic is too complicated to calculate the calculation due to the containing a large number of bilateral positive cut functions. Therefore, this article proposes a LDPC decoding algorithm that combines the Modified Weighted Bit Flipping Decoding Algorithm (MWBF) with the Logarithmic Likelihood Ratio Based Confidence Propagation Algorithm (LLR-BP). In the process of the LLR-BP decoding iteration, the introduction of the MWBF algorithm not only does it reduce the complexity of iteration and decoding, but also avoids the traditional weighted bit-flipping decoding algorithm (WBF). Excessive effects of code performance.
Emergency dispatching method of unmanned vehicle based on shortest path algorithm

The problem of path planning has attracted wide attention. Dispatching is also an extremely essential link in the field of train transportation, especially in the case of emergency, dispatching is more significant. When resolving the dispatching problem, this paper seeks the shortest path based on Dijkstra’s algorithm and sets the functional logic that the dispatching needs to follow. Finally, the experiments indicate that the dispatching method in this paper has achieved excellent outcomes.
Research on added waves resistance in misalignment paralleling of two ships

Motivated by the importance of slow-speed underway replenishment of vessels in offshore shipping, this study aims to investigate the interaction and wave resistance phenomena between two ships sailing in close proximity, which may lead to lateral drift, yaw, and even collision, posing a great threat to the safety of both vessels. KVLCC2 and Aframax standard model ships were chosen as the research objects. To examine the ship-ship interaction and wave resistance phenomena in regular waves under the conditions of low-speed single ship and staggered navigation, RANS numerical simulation method and overset grid technique were employed. The simulation results indicate that the ship's motion and forces and moments between two ships are sensitive to the wave's wavelength, especially in the range of λ/Lpp=1~1.5, where response peaks were observed. Compared to single-ship navigation, the longitudinal pitching effect of two ships sailing staggered in waves is significant and increases with wave height. This study provides an important reference value for the safe navigation of ships during wave-induced staggered navigation and replenishment operations at sea.
Scalable k-nearest neighbor algorithms based on learned functions

Approximate k-Nearest Neighbor (kNN) is a fundamental problem in data science. A lot of traditional approaches have been devel- oped to solve this problem, yet none of them can achieve high accuracy, small memory usage and fast query speed. These methods can be in- efficient since they don’t improve with experience(learning from errors or heuristics employed). A recent trend in algorithm design consists of augmenting classic data structures with machine learning models. Mo- tivated by this, we present a learned index for kNN query in massive amounts of data, named LBF. It robustly combines a traditional index with a learned model. What’s more, a multiple feature extraction mod- ule is embedded into our model to make full use of the raw input. These make our method satisfies all the requirements of a good similarity in- dexing scheme. Theoretical proofs and experimental results on a variety of datasets show that compared to the state-of-the-art methods, we can achieve a comparable accuracy with much less memory usage.
3D UAV adaptive deployment and energy saving scheduling scheme for persistent communication

In recent years, Unmanned Aerial Vehicle (UAV) has attracted much attention as a communication platform with great potential. Benefiting mobility, UAV aerial base stations can be deployed quickly and flexibly, effectively establishing line-of-sight communication links. However, due to the limitation of its onboard energy, the UAV network's durability has become a significant challenge. This paper uses ground supply facilities to recharge the system in a replacement manner continuously and studies how to deploy UAVs and energy-saving scheduling adaptively. Firstly, A UAV 3D deployment scheme is designed based on the K-means algorithm. Under the constraint of the maximum tolerable delay, it is dynamically deployed according to the user's location information, including the number and coordinates of drones. Secondly, by monitoring the energy of UAVs, the energy-saving scheduling of UAV groups is carried out in a centralized manner. Finally, the scheme's feasibility is verified through simulation experiments, which significantly improves the UAV utilization rate and effectively reduces the system energy consumption.
A face recognition method based on sparse enhancement and collaborative information fusion for epidemic prevention and control

At present, "COVID-19" has spread in 184 countries (regions) in the world, and more than 500 million patients have been diagnosed and caused 66.3 million deaths. The worldwide epidemic is still ongoing, and the epidemic in China is still increasing, therefore, the regular prevention and control of the epidemic is An important part of ensuring people live, live, live, especially in crowded places, there is an urgent need for artificial intelligence, biometrics and other technologies. This paper proposes the face recognition method of sparse enhancement and collaborative information fusion. The paper first explains and generalises the relevant terms, and finally, the practicality of the algorithm is demonstrated through experimental analysis. The recognition rate of the algorithm described in this paper is generally higher than 95% on different facial masking ratios, and the results show that the face recognition method of sparse enhancement and collaborative information fusion based on epidemic prevention and control is a very worthy research The results show that face recognition based on sparse enhancement and collaborative information fusion for epidemic prevention and control is a very worthwhile research technique that can help solve the critical problems of epidemic prevention and control today.
Registration of thermal 3D models over time using low cost and portable devices

Thermography is a highly beneficial non-invasive and non-contact tool that finds applications in various fields, such as building inspection, industrial equipment monitoring, quality control, and medical evaluations. Analyzing the surface temperature of an object at different points in time, and under varying conditions, can help detect defects, cracks, and anomalies in industry components. In this study, we propose a framework for reproducible and quantitative measurement of surface temperature changes over time using thermal 3D models created with low-cost and portable devices. We present the application of this framework in two cases: to analyze temperature changes over time in a plastic container and to analyze temperature changes before and after medical treatment of a chronic wound. The results on a plastic container and on a chronic wound, show that our approach for multi-temporal registration of thermal 3D models could be a cost-effective and practical solution for studying temperature changes in various applications.