Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution from Bioinert to Bioresorbable: In Vivo Comparative Study of Additively Manufactured Metal Bone Scaffolds

Additively manufactured scaffolds offer significant potential for treating bone defects, owing to their porous, customizable architecture and functionalization capabilities. Although various biomaterials have been investigated, metals – the most successful orthopedic material – have yet to yield satisfactory results. Conventional bio‐inert metals, such as titanium (Ti) and its alloys, are widely used for fixation devices and reconstructive implants, but their non‐bioresorbable nature and the mechanical property mismatch with human bones limit their application as porous scaffolds for bone regeneration. Advancements in additive manufacturing have facilitated the use of bioresorbable metals, including magnesium (Mg), zinc (Zn), and their alloys, as porous scaffolds via Laser Powder Bed Fusion (L‐PBF) technology. This in vivo study presents a comprehensive, side‐by‐side comparative analysis of the interactions between bone regeneration and additively manufactured bio‐inert/bioresorbable metal scaffolds, as well as their therapeutic outcomes. The research offers an in‐depth understanding of the metal scaffold‐assisted bone healing process, illustrating that Mg and Zn scaffolds contribute to the bone healing process in distinct ways, but ultimately deliver superior therapeutic outcomes compared to Ti scaffolds. These findings suggest that bioresorbable metal scaffolds hold considerable promise for the clinical treatment of bone defects in the near future.
Fucose promotes intestinal stem cell-mediated intestinal epithelial development through promoting Akkermansia-related propanoate metabolism

ABSTRACT Intestinal stem cells (ISCs) are critical for the development and rapid turnover of intestinal epithelium. The regulatory effects of gut microbiota and their metabolites on ISCs stemness remain elusive. Fucose has been demonstrated to mediate host–microbe interactions in the intestine. However, the association between fucose, gut bacteria, and ISCs stemness remains unclear. To investigate the effects of fucose on ISCs-mediated intestinal epithelial cells (IECs) development, we administered fucose to 4-week-old mice for 4 weeks. ISCs stemness, IECs proliferation, and differentiation were examined. Variations in gut microbes and metabolism were detected using 16S rDNA sequencing and metabolomic analysis. Fucose was added to the bacterial culture medium to further study its effects on metabolism. Crypts were isolated from the mouse ileum for organoids culture in vitro to evaluate the effects of metabolites and the underlying mechanism. The results showed that fucose accelerated ISCs proliferation and secretory lineage differentiation in mice, whereas antibiotics eliminated these effects. The composition and functions of gut bacteria were altered by fucose treatment, while significant increases in Akkermansia and propanoate metabolism were noted. Propionic acid and propionate have been shown to promote organoid development. Fucose fermentation increases the production of propionic acid in Akkermansia muciniphila and enhances its ability to increase the stemness of ISCs. Moreover, ileal contents from fucose-treated mice promoted organoid development in a Gpr41/Gpr43-dependent manner. Fucose administration activates the Wnt signaling pathway in ISCs, and Wnt inhibitors suppress the effects of fucose. We conclude that fucose accelerates ISC-mediated intestinal epithelial development by promoting Akkermansia-related propanoate metabolism. These findings provide new insights into the promotion of gut homeostasis and the application potential of fucose as a prebiotic.
Survival rates and mortality risks of Plecturocebus cupreus at the California National Primate Research Center

This article describes survivorship and explores factors affecting mortality risks in a captive colony of coppery titi monkeys (Plecturocebus cupreus) housed at the California National Primate Research Center (CNPRC), at UC Davis, in Davis, CA. We analyzed data collected on individuals since the colony's creation in the 1960s, with a sample of 600 animals with partially complete information (date of birth, age at death, body mass, parental lineage). We used three methods: (1) Kaplan–Meier regressions followed by a log‐rank test to compare survival in male and female titi monkeys, (2) a breakpoint analysis to identify shifts in the survival curves, and (3) Cox regressions to test the effect of body mass change, parental pair tenure, and parental age on mortality risk. We found that males tend to have a longer median lifespan than females (14.9 and 11.4 years; p = 0.094) and that survival decreases earlier in males than in females during adulthood (9.8 and 16.2 years). A body mass loss of 10% from adulthood to the time of death led to a 26% higher risk of dying (p < 0.001) as compared to an individual with stable body mass. We found no evidence of sociobiological factors on mortality risks (parental age, parental pair tenure), but an exploratory analysis suggested that a higher rate of offspring conceptions increases mortality risks. This description of factors influencing survival and mortality in titi monkeys is a first step toward understanding aging in this species to consider titi monkeys as a primate model for socioemotional aging.
Management of covert brain infarction survey: A call to care for and trial this neglected population

Background: Covert brain infarction (CBI) is highly prevalent and linked with stroke risk factors, increased mortality, and morbidity. Evidence to guide management is sparse. We sought to gain information on current practice and attitudes toward CBI and to compare differences in management according to CBI phenotype. Methods: We conducted a web-based, structured, international survey from November 2021 to February 2022 among neurologists and neuroradiologists. The survey captured respondents’ baseline characteristics, general approach toward CBI and included two case scenarios designed to evaluate management decisions taken upon incidental detection of an embolic-phenotype and a small-vessel-disease phenotype. Results: Of 627 respondents (38% vascular neurologists, 24% general neurologists, and 26% neuroradiologists), 362 (58%) had a partial, and 305 (49%) a complete response. Most respondents were university hospital senior faculty members experienced in stroke, mostly from Europe and Asia. Only 66 (18%) of respondents had established institutional written protocols to manage CBI. The majority indicated that they were uncertain regarding useful investigations and further management of CBI patients (median 67 on a slider 0–100, 95% CI 35–81). Almost all respondents (97%) indicated that they would assess vascular risk factors. Although most would investigate and treat similarly to ischemic stroke for both phenotypes, including initiating antithrombotic treatment, there was considerable diagnostic and therapeutic heterogeneity. Less than half of respondents (42%) would assess cognitive function or depression. Conclusions: There is a high degree of uncertainty and heterogeneity regarding management of two common types of CBI, even among experienced stroke physicians. Respondents were more proactive regarding the diagnostic and therapeutic management than the minimum recommended by current expert opinions. More data are required to guide management of CBI; meantime, more consistent approaches to identification and consistent application of current knowledge, that also consider cognition and mood, would be promising first steps to improve consistency of care.
Detection of homogeneous wheat areas using multi-temporal UAS images and ground truth data analyzed by cluster analysis

ABSTRACT Vegetation indices (VIs) obtained from unmanned aerial system (UAS) are effective for monitoring quantitative and qualitative characteristics of vegetation cover. Nevertheless, the identification of agronomic homogeneous crop areas to be managed in a specific different agronomic way is still to be improved in precision farming. The aim of the study was to reduce information gap on the detection of homogeneous wheat areas using multi-temporal remote sensing image and agronomic crop traits by cluster analysis. The images were acquired by an eBee UAS on a small plot of 720 m2 at three different crop growth stages High-resolution orthoimages (3.5 cm·pixel−1) were generated by Pix4D and QGIS. At each growth stage, biometric ground-truth data and VIs (NDVI and SAVI) were clustered to detect homogeneous crop areas. At tillering and anthesis stages, three significant homogeneous areas with low (L) medium (M) and high (H) VIs and agronomical values were identified for both indices. Yield-related traits (at harvest) and VIs (at anthesis) confirmed that L and M areas, with agronomic constraints identified at anthesis, showed crop yield losses at harvest. Cluster analysis, using UAS and ground truth data, has proved to be a good strategy to identify the homogeneous wheat crop areas.
Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space

Kernel-based regularized learning seeks a model in a hypothesis space by minimizing the empirical error and the model s complexity. Based on the representer theorem, the solution consists of a linear combination of translates of a kernel. This paper investigates a generalized form of representer theorem for kernel-based learning. After mapping prede ned features and translates of a kernel simultaneously onto a hypothesis space by a specific way of constructing kernels, we proposed a new algorithm by utilizing a generalized regularizer which leaves part of the space unregularized. Using a squared-loss function in calculating the empirical error, a simple convex solution is obtained which combines predefined features with translates of the kernel. Empirical evaluations have confirmed the effectiveness of the algorithm for supervised learning tasks.
Efficient and effective quantum compiling for entanglement-based machine learning on IBM Q devices

Quantum compiling means fast, device-aware implementation of quantum algorithms (i.e. quantum circuits, in the quantum circuit model of computation). In this paper, we present a strategy for compiling IBM Q-aware, low-depth quantum circuits that generate Greenberger–Horne–Zeilinger (GHZ) entangled states. The resulting compiler can replace the QISKit compiler for the specific purpose of obtaining improved GHZ circuits. It is well known that GHZ states have several practical applications, including quantum machine learning. We illustrate our experience in implementing and querying a uniform quantum example oracle based on the GHZ circuit, for solving the classically hard problem of learning parity with noise.
Understanding the Patient Experience of Foreign-Born Older Adults: A Scoping Review of Older Immigrants Receiving Health Care in Canada

Abstract In Canada, foreign-born older adults (FBOAs) have a higher prevalence of chronic conditions and poorer self-reported physical and mental health than their Canadian-born peers. However, very little research has explored FBOAs’ experiences of health care after immigration. This review aims to understand the patient experiences of older immigrants within the Canadian health care system. Employing Arksey and O’Malley’s framework for scoping reviews, we searched six databases and identified 12 articles that discussed the patient experience of this population. Although we sought to understand patient experience, the studies largely focused on barriers to care, including: communication difficulties, lack of cultural integration, systematic barriers in health care, financial barriers, and intersecting barriers related to culture and gender. This review provides insight into new areas of research and advocates for strengthened policy and/or programming. Our review also highlights that there is a paucity of literature for an ever-growing segment of the Canadian population.
High-Order Maximum Principle Preserving (MPP) Techniques for Solving Conservation Laws with Applications on Multiphase Flow

We develop numerical methods to solve the linear scalar conservation law fulfilling the maximum principle. To do this we use continuous and discontinuous Galerkin finite elements and achieve the preservation on the maximum principle via the Flux Corrected Transport (FCT) method. We use high-order polynomial spaces with Bernstein basis functions and obtain the optimal convergence rates with spaces of up to third order for smooth solutions that are monotone. This methodology produces good quality results for spaces up to (around) third order. However, when higher-order spaces are used non-physical oscillations are introduced, which is true nevertheless the methods are maximum principle preserving. These oscillations can be highly reduced by defining tighter bounds. Using discontinuous Galerkin finite elements we present a new FCT-like methodology based on single cell flux corrections. This method combines a mass conservative low-order Maximum Principle Preserving (MPP) solution with a non mass conservative high-order MPP solution. The process is designed to recover mass conservation locally (with respect to degrees of freedom). Using this scheme we obtain the optimal convergence rates with spaces of up to third order for smooth solutions that are monotone. The method is designed to overcome problems when high-order spaces are used and, under this context, we obtained better results than with the standard FCT method. We present two methods to transport a smoothed Heaviside level set function using a one-stage reinitialization based on artificial compression. The first method allows arbitrarily large compression which might lead to non-physical behavior. To over come this difficulty the second method self balances the artificial dissipation and compression. Finally, we use the level set solver with a Navier-Stokes solver to simulate incompressible two-phase flow.
Anthelmintic nitazoxanide protects against experimental pulmonary fibrosis

Nitazoxanide is a therapeutic anthelmintic drug. Our previous studies found that nitazoxanide and its metabolite tizoxanide activated adenosine 5′‐monophosphate‐activated protein kinase (AMPK) and inhibited signal transducer and activator of transcription 3 (STAT3) signals. As AMPK activation and/or STAT3 inhibition are targets for treating pulmonary fibrosis, we hypothesized that nitazoxanide would be effective in experimental pulmonary fibrosis.
Radiomics-based Machine Learning Methods for Volume Doubling Time Prediction of Pulmonary Ground-glass Nodules With Baseline Chest Computed Tomography

Purpose: Reliable prediction of volume doubling time (VDT) is essential for the personalized management of pulmonary ground-glass nodules (GGNs). We aimed to determine the optimal VDT prediction method by comparing different machine learning methods only based on the baseline chest computed tomography (CT) images. Materials and Methods: Seven classical machine learning methods were evaluated in terms of their stability and performance for VDT prediction. The VDT, calculated by the preoperative and baseline CT, was divided into 2 groups with a cutoff value of 400 days. A total of 90 GGNs from 3 hospitals constituted the training set, and 86 GGNs from the fourth hospital served as the external validation set. The training set was used for feature selection and model training, and the validation set was used to evaluate the predictive performance of the model independently. Results: The eXtreme Gradient Boosting showed the highest predictive performance (accuracy: 0.890±0.128 and area under the ROC curve (AUC): 0.896±0.134), followed by the neural network (NNet) (accuracy: 0.865±0.103 and AUC: 0.886±0.097). While regarding stability, the NNet showed the highest robustness against data perturbation (relative SDs [%] of mean AUC: 10.9%). Therefore, the NNet was chosen as the final model, achieving high accuracy of 0.756 in the external validation set. Conclusion: The NNet is a promising machine learning method to predict the VDT of GGNs, which would assist in the personalized follow-up and treatment strategies for GGNs reducing unnecessary follow-up and radiation dose.
Consensus of first-order agents with time-varying communication delay

In this paper, we consider the consensus problems for first-order multi-agent systems with communication delay. Under the assumption that the delay is time-varying and unknown, allowable delay bound depending on agent dynamic, control gain and the eigenvalues of the Laplacian matrix are obtained over undirected network topology. Especially, if the agent dynamics are first-order integrator systems, any large and bounded communication delay is allowed for consensus. Finally, a numerical example is given to validate the theoretical results.
Knock‐out of multidrug efflux pump MexXY‐OprM results in increased susceptibility to antimicrobial peptides in Pseudomonas aeruginosa

Multidrug efflux systems of the resistance‐nodulation‐cell division family play a crucial role in resistance of Pseudomonas aeruginosa to a large variety of antibiotics. Here, we investigated the role of clinically relevant efflux pumps MexAB−OprM, MexCD−OprJ, and MexXY−OprM in resistance against different cationic antimicrobial peptides (AMPs). Our results indicate that a knock‐out in efflux pump MexXY‐OprM increased susceptibility to some AMPs by two‐ to eightfold. Our data suggest a contribution of MexXY‐OprM in resistance to certain AMPs in P. aeruginosa, which should be considered in the future development of new and highly active antimicrobial peptides to fight multidrug resistant infections.
Global and Local Visual Processing: Influence of Perceptual Field Variables

The Global Precedence Effect (GPE) suggests that the processing of global properties of a visual stimulus precedes the processing of local properties. The generality of this theory was argued for four decades during different known Perceptual Field Variables. The effect size of various PFVs, regarding the findings during these four decades, were pooled in our recent meta-analysis study. Pursuing the study, in the present paper, we explore the effects of Congruency, Size, and Sparsity and their interaction on global advantage in two different experiments with different task paradigms; Matching judgment and Similarity judgment. Upon results of these experiments, Congruency and Size have significant effects and Sparsity has small effects. Also, the task paradigm and its interaction with other PFVs are shown significant effects in this study, which shows the prominence of the role of task paradigms in evaluating PFVs' effects on GPE. Also, we found that the effects of these parameters were not specific to the special condition that individuals were instructed to retinal stabilize. So, the experiments were more extendible to daily human behavior.
Internet Localization of Multi-Party Relay Users: Inherent Friction Between Internet Services and User Privacy

Internet privacy is increasingly important on the modern Internet. Users are looking to control the trail of data that they leave behind on the systems that they interact with. Multi-Party Relay (MPR) architectures lower the traditional barriers to adoption of privacy enhancing technologies on the Internet. MPRs are unique from legacy architectures in that they are able to offer privacy guarantees without paying significant performance penalties. Apple's iCloud Private Relay is a recently deployed MPR service, creating the potential for widespread consumer adoption of the architecture. However, many current Internet-scale systems are designed based on assumptions that may no longer hold for users of privacy enhancing systems like Private Relay. There are inherent tensions between systems that rely on data about users -- estimated location of a user based on their IP address, for example -- and the trend towards a more private Internet. This work studies a core function that is widely used to control network and application behavior, IP geolocation, in the context of iCloud Private Relay usage. We study the location accuracy of popular IP geolocation services compared against the published location dataset that Apple publicly releases to explicitly aid in geolocating PR users. We characterize geolocation service performance across a number of dimensions, including different countries, IP version, infrastructure provider, and time. Our findings lead us to conclude that existing approaches to IP geolocation (e.g., frequently updated databases) perform inadequately for users of the MPR architecture. For example, we find median location errors>1,000 miles in some countries for IPv4 addresses using IP2Location. Our findings lead us to conclude that new, privacy-focused, techniques for inferring user location may be required as privacy becomes a default user expectation on the Internet.
PCG-based Static Underground Garage Scenario Generation

Autonomous driving technology has five levels, from L0 to L5. Currently, only the L2 level (partial automation) can be achieved, and there is a long way to go before reaching the final level of L5 (full automation). The key to crossing these levels lies in training the autonomous driving model. However, relying solely on real-world road data to train the model is far from enough and consumes a great deal of resources. Although there are already examples of training autonomous driving models through simulators that simulate real-world scenarios, these scenarios require complete manual construction. Directly converting 3D scenes from road network formats will lack a large amount of detail and cannot be used as training sets. Underground parking garage static scenario simulation is regarded as a procedural content generation (PCG) problem. This paper will use the Sarsa algorithm to solve procedural content generation on underground garage structures.
Causal Neural Graph Collaborative Filtering

Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF) models. One classical approach in GCF is to learn user and item embeddings by modeling complex graph relations and utilizing these embeddings for CF models. However, the quality of the embeddings significantly impacts the recommendation performance of GCF models. In this paper, we argue that existing graph learning methods are insufficient in generating satisfactory embeddings for CF models. This is because they aggregate neighboring node messages directly, which can result in incorrect estimations of user-item correlations. To overcome this limitation, we propose a novel approach that incorporates causal modeling to explicitly encode the causal effects of neighboring nodes on the target node. This approach enables us to identify spurious correlations and uncover the root causes of user preferences. We introduce Causal Neural Graph Collaborative Filtering (CNGCF), the first causality-aware graph learning framework for CF. CNGCF integrates causal modeling into the graph representation learning process, explicitly coupling causal effects between node pairs into the core message-passing process of graph learning. As a result, CNGCF yields causality-aware embeddings that promote robust recommendations. Our extensive experiments demonstrate that CNGCF provides precise recommendations that align with user preferences. Therefore, our proposed framework can address the limitations of existing GCF models and offer a more effective solution for recommendation systems.