Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Oral Iron Supplementation/Fortification on the Gut Microbiota in Infancy: A Systematic Review and Meta-Analysis

(1) Background: Iron is an essential metal for the proper growth and neurodevelopment of infants. To prevent and treat iron deficiency, iron supplementation or fortification is often required. It has been shown, though, that it affects the synthesis of gut microbiota. (2) Methods: This paper is a systematic review and meta-analysis of the effect of oral iron supplementation/fortification on the gut microbiota in infancy. Studies in healthy neonates and infants who received per os iron with existing data on gut microbiota were included. Three databases were searched: PUBMED, Scopus, and Google Scholar. Randomized controlled trials (RCTs) were included. Quality appraisal was assessed using the ROB2Tool. (3) Results: A total of six RCTs met inclusion criteria for a systematic review, and four of them were included in the meta-analysis using both the fixed and random effects methods. Our results showed that there is very good heterogeneity in the iron group (I2 = 62%), and excellent heterogeneity in the non-iron group (I2 = 98%). According to the meta-analysis outcomes, there is a 10.3% (95% CI: −15.0–−5.55%) reduction in the bifidobacteria population in the iron group and a −2.96% reduction for the non-iron group. There is a confirmed difference (p = 0.02) in the aggregated outcomes between iron and non-iron supplement, indicative that the bifidobacteria population is reduced when iron supplementation is given (total reduction 6.37%, 95%CI: 10.16–25.8%). (4) Conclusions: The abundance of bifidobacteria decreases when iron supplementation or fortification is given to infants.
PSMOT: Online Occlusion-Aware Multi-Object Tracking Exploiting Position Sensitivity

Models based on joint detection and re-identification (ReID), which significantly increase the efficiency of online multi-object tracking (MOT) systems, are an evolution from separate detection and ReID models in the tracking-by-detection (TBD) paradigm. It is observed that these joint models are typically one-stage, while the two-stage models become obsolete because of their slow speed and low efficiency. However, the two-stage models have naive advantages over the one-stage anchor-based and anchor-free models in handling feature misalignment and occlusion, which suggests that the two-stage models, via meticulous design, could be on par with the state-of-the-art one-stage models. Following this intuition, we propose a robust and efficient two-stage joint model based on R–FCN, whose backbone and neck are fully convolutional, and the RoI-wise process only involves simple calculations. In the first stage, an adaptive sparse anchoring scheme is utilized to produce adequate, high-quality proposals to improve efficiency. To boost both detection and ReID, two key elements—feature aggregation and feature disentanglement—are taken into account. To improve robustness against occlusion, the position-sensitivity is exploited, first to estimate occlusion and then to direct the post-process for anti-occlusion. Finally, we link the model to a hierarchical association algorithm to form a complete MOT system called PSMOT. Compared to other cutting-edge systems, PSMOT achieves competitive performance while maintaining time efficiency.
Greenhouse Climatic Sensing through Agricultural Robots and Recurrent Neural Networks

In today’s greenhouse farming the assumption of climatic uniformity is no more acceptable since it may lead to sub-optimal decisions; however, the deployment of a sensor for each point of interest is expensive and incompatible with field operations. Agricultural robots are quickly moving from the research field to real scenarios for weed removal, harvesting and other agronomic operations. In this paper, we show the possibility, enabled by Recurrent Neural Networks, to exploit successive samplings of the mobile robot in its wandering in the greenhouse to infer the value of the desired climatic variable in specific points of interest. More specifically, we use recurrent neural networks to model the dependency of the climate conditions in each point of interest as a function of the distance with respect to various points on robot’s trajectory. The proposed technique has been applied to a real dataset coming from a greenhouse located near Verona and compared with traditional approaches such as considering a single sensor located at the center of the greenhouse and compressed sensing.
Estimation des stocks de carbone dans les sols de Madagascar

Impact of agriculture and forestry development projects, programmes and policies on the GHG and carbon-balance: The EX-Ante Carbon-balance Tool (EX-ACT) View project Séquestration du carbone et disponibilité des nutriments (phosphore et azote) des sols des différents agroécosystèmes dans le cadre de la REDD+ à Madagascar View project Clovis Grinand Etc Terra 20 PUBLICATIONS 387 CITATIONS SEE PROFILE Martial Bernoux Food and Agriculture Organization of the United Nations 248 PUBLICATIONS 6,233 CITATIONS SEE PROFILE
Improving shared decision-making about cancer treatment through design-based data-driven decision-support tools and redesigning care paths: an overview of the 4D PICTURE project

Background: Patients with cancer often have to make complex decisions about treatment, with the options varying in risk profiles and effects on survival and quality of life. Moreover, inefficient care paths make it hard for patients to participate in shared decision-making. Data-driven decision-support tools have the potential to empower patients, support personalized care, improve health outcomes and promote health equity. However, decision-support tools currently seldom consider quality of life or individual preferences, and their use in clinical practice remains limited, partly because they are not well integrated in patients’ care paths. Aim and objectives: The central aim of the 4D PICTURE project is to redesign patients’ care paths and develop and integrate evidence-based decision-support tools to improve decision-making processes in cancer care delivery. This article presents an overview of this international, interdisciplinary project. Design, methods and analysis: In co-creation with patients and other stakeholders, we will develop data-driven decision-support tools for patients with breast cancer, prostate cancer and melanoma. We will support treatment decisions by using large, high-quality datasets with state-of-the-art prognostic algorithms. We will further develop a conversation tool, the Metaphor Menu, using text mining combined with citizen science techniques and linguistics, incorporating large datasets of patient experiences, values and preferences. We will further develop a promising methodology, MetroMapping, to redesign care paths. We will evaluate MetroMapping and these integrated decision-support tools, and ensure their sustainability using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework. We will explore the generalizability of MetroMapping and the decision-support tools for other types of cancer and across other EU member states. Ethics: Through an embedded ethics approach, we will address social and ethical issues. Discussion: Improved care paths integrating comprehensive decision-support tools have the potential to empower patients, their significant others and healthcare providers in decision-making and improve outcomes. This project will strengthen health care at the system level by improving its resilience and efficiency.
Unveiling Algorithm Classification Excellence: Exploring Calendula and Coreopsis Flower Datasets with Varied Segmentation Techniques

This investigation constitutes a noteworthy progression in the advancement of more sophisticated and precise botanical image analysis. The primary objective of this inquiry is to confront the difficulties associated with the categorization of Calendula and Coreopsis flowers through the application of diverse segmentation techniques and classification algorithms. In this experiment, we employed the Canny edge detection, thresholding, mean shift, and Otsu methods to process flower images before applying Naïve Bayes, K-Nearest Neighbors, Support Vector Machine, and Decision Tree algorithms for classification. Enhanced comprehension of the integration of distinct segmentation techniques with varied classification algorithms is attained. We scrutinized accuracy, precision, recall, and F1 measure across diverse segmentation scenarios to assess the efficacy of these algorithms. Our principal discoveries consistently affirm that the Decision Tree algorithm attains the utmost accuracy levels in flower classification when coupled with mean shift segmentation, underscoring its noteworthy proficiency in this endeavor. The pivotal role of an optimal amalgamation of segmentation techniques and classification algorithms in augmenting flower recognition is underscored, thereby charting the course for subsequent investigations into the integration of diverse segmentation methods with advanced classification algorithms. This study's outcomes wield a favorable influence on the domain of botany and image analysis at large, offering support to researchers and scientists in achieving a more precise understanding and classification of plant species.
Energy Efficiency is Not Enough!

In its latest report, the Intergovernmental Panel on Climate Change (IPCC) concludes that any further increase of global temperatures aggravates climate-related risks like species losses, extreme heat and humidity with significant consequences for human health, or negative impacts on food production and water availability; the projected negative outcomes escalate with higher temperature increases [1]. Therefore, worldwide efforts and international policymaking (i.e., The Paris Agreement from 2015) aim at limiting global warming by the end of the 21st century to well below +2°C above pre-industrial levels, preferably to not more than +1.5°C.
Distributed Multiple LEO Satellites Cooperative Downlink Power Enhancement Transmission Scheme Based on OTFS

Aiming at the problem of satellite power limitation, this paper proposes a distributed multi-satellite cooperative transmission (DMCT) scheme based orthogonal time frequency space (OTFS) for low earth orbit (LEO) satellite networks to achieve downlink power and transmission performance enhancement. Firstly, we construct the DMCT system model and channel model. Through theoretical analysis, it is found that the dynamic change of phase difference between each data stream from cooperative satellite cluster arriving at the terminal will cause serious time-varying fast fading. To this end, we design the OTFS transmission model for DMCT systems, and present the subspace pursuit (SP) channel estimation method and message passing (MP) data detection method taking advantage of the sparsity of the DMCT channel. Simulation results show that the proposed scheme can significantly enhance downlink power and transmission performance.
Two Main Cancer Biomarkers as Molecular Targets of Binase Antitumor Activity

Cancer is frequently coupled with the disturbance of key signaling pathways. Aberrant activation of the mitogen-activated protein kinase (MAPK) signaling cascade, occurring in over 85% of cancers, is mainly caused by the genetic alterations of its main components—oncogenes EGFR and RAS, and plays a crucial role in cell fate. The importance of EGFR and RAS proteins in a variety of tumors suggests that they would be good therapeutic targets, but at present, no effective targeted therapy against these two oncogenes has been proven. Here, we show that ribonuclease from Bacillus pumilus (binase) inhibits MAPK signaling through direct interaction with EGFR and RAS proteins. This effect contributes to the antitumor potential of binase along with its enzymatic activity. Multitargeticity of binase prevents the development of drug resistance, which is considered a major obstacle to effective anticancer treatment.
Path Planning Method for Power Inspection Robots Based on the Integration of A* Algorithm and Dynamic Window Approach

This study introduces a novel obstacle avoidance technique for power inspection robots. The approach combines the A* algorithm with the Dynamic Window Approach (DWA) to enhance the robot's ability to navigate around obstacles. First, we improve the A* method by optimizing the evaluation function and implementing a redundant node removal strategy. This enhancement not only enhances the search efficiency but also eliminates redundant nodes. Second, the DWA is utilized for local planning between each pair of adjacent nodes, enabling real-time random obstacle avoidance. Finally, we conduct simulation verification in the MATLAB to validate our approach. The results demonstrate that the improved A* method reduces the path length. Furthermore, with the integration of DWA, it is capable of adjusting the local path and achieving random obstacle avoidance.
Three-Dimensional Bioprinting of Strontium-Modified Controlled Assembly of Collagen Polylactic Acid Composite Scaffold for Bone Repair

In recent years, the incidence of bone defects has been increasing year by year. Bone transplantation has become the most needed surgery after a blood transfusion and shows a rising trend. Three-dimensional-printed implants can be arbitrarily shaped according to the defects of tissues and organs to achieve perfect morphological repair, opening a new way for non-traumatic repair and functional reconstruction. In this paper, strontium-doped mineralized collagen was first prepared by an in vitro biomimetic mineralization method and then polylactic acid was homogeneously blended with the mineralized collagen to produce a comprehensive bone repair scaffold by a gas extrusion 3D printing method. Characterization through scanning electron microscopy, X-ray diffraction, and mechanical testing revealed that the strontium-functionalized composite scaffold exhibits an inorganic composition and nanostructure akin to those of human bone tissue. The scaffold possesses uniformly distributed and interconnected pores, with a compressive strength reaching 21.04 MPa. The strontium doping in the mineralized collagen improved the biocompatibility of the scaffold and inhibited the differentiation of osteoclasts to promote bone regeneration. This innovative composite scaffold holds significant promise in the field of bone tissue engineering, providing a forward-thinking solution for prospective bone injury repair.
Tensorial Hankel Reconstruction for Coherent DOA Estimation with Sensor Failure

The research on coherent direction of arrival (DOA) estimation is of great significance for high-precision DOA estimation in complex electromagnetic environments. However, existing coherent DOA estimation methods do not consider the issue of sensor failure that is prone to occur under harsh environments. In this paper, we propose a tensorial Hankel reconstruction method for coherent DOA estimation with sensor failure. Specifically, by analyzing the tensorial Hankel property of complete incoherent covariance tensor, we design the tensorial Hankel mapping rule to reconstruct a Hankel tensor from a slice of incomplete coherent covariance tensor. Meanwhile, the fiber-missing elements caused by sensor failure are dispersed to higher dimensions in the reconstruction process, ensuring the effectiveness of conventional low-rank tensor completion algorithm. Then, the completed Hankel tensor is proven to admit the canonical polyadic model, which can be decomposed to obtain DOA estimations. Simulation results confirm the advantage of the proposed tensorial Hankel reconstruction method.
Identification of a gene network driving the attenuated response to lipopolysaccharide of monocytes from hypertensive coronary artery disease patients

Introduction The impact of cardiovascular disease (CVD) risk factors, encompassing various biological determinants and unhealthy lifestyles, on the functional dynamics of circulating monocytes—a pivotal cell type in CVD pathophysiology remains elusive. In this study, we aimed to elucidate the influence of CVD risk factors on monocyte transcriptional responses to an infectious stimulus. Methods We conducted a comparative analysis of monocyte gene expression profiles from the CTMM – CIRCULATING CELLS Cohort of coronary artery disease (CAD) patients, at baseline and after lipopolysaccharide (LPS) stimulation. Gene co-expression analysis was used to identify gene modules and their correlations with CVD risk factors, while pivotal transcription factors controlling the hub genes in these modules were identified by regulatory network analyses. The identified gene module was subjected to a drug repurposing screen, utilizing the LINCS L1000 database. Results Monocyte responsiveness to LPS showed a highly significant, negative correlation with blood pressure levels (ρ< -0.4; P<10-80). We identified a ZNF12/ZBTB43-driven gene module closely linked to diastolic blood pressure, suggesting that monocyte responses to infectious stimuli, such as LPS, are attenuated in CAD patients with elevated diastolic blood pressure. This attenuation appears associated with a dampening of the LPS-induced suppression of oxidative phosphorylation. Finally, we identified the serine-threonine inhibitor MW-STK33-97 as a drug candidate capable of reversing this aberrant LPS response. Conclusions Monocyte responses to infectious stimuli may be hampered in CAD patients with high diastolic blood pressure and this attenuated inflammatory response may be reversed by the serine-threonine inhibitor MW-STK33-97. Whether the identified gene module is a mere indicator of, or causal factor in diastolic blood pressure and the associated dampened LPS responses remains to be determined.
Boundary Element Analysis of Two Dimensional Structure Water Entry Based on Attached Water Quality

Combining nonlinear boundary conditions of free liquid surface, the impact force of wedge shape profile and the change of free liquid surface were calculated and analyzed based on the solving ideas of Von Karman T. The results were compared with the results of the existing literature, and the results were in good agreement with the existing results. In addition, the impact forces of U profile and the external drift profile were calculated, and the impact load of two kinds of profiles were analyzed through time evolution.
Fragile X Mental Retardation Protein regulates R-loop formation and prevents global chromosome fragility

Fragile X syndrome (FXS) is the most prevalent inherited intellectual disability caused by mutations in the Fragile X Mental Retardation 1 (FMR1) gene. The protein product of FMR1, FMRP, is known as a translational repressor whose nuclear function is not understood. Here we report that FMRP is a genome maintenance protein. We show that FX cells exhibit elevated level of chromosome breaks, both spontaneous and replication stress-induced. We demonstrate that FMRP is required for abating R-loop accumulation, thereby preventing chromosome breakage. Through mapping FMRP-bound chromatin loci in normal cells and correlating with FX-specific chromosome breaks, we identified novel FXS-susceptible genes. We show that FX cells have reduced expression of the uridine diphosphoglucuronosyl transferase 1 family enzymes, suggesting defective xenobiotics glucuronidation and consequential neurotoxicity in FXS.
Astrobee ISS Free-Flyer Datasets for Space Intra-Vehicular Robot Navigation Research

We present the first annotated benchmark datasets for evaluating free-flyer visual-inertial localization and mapping algorithms in a zero-g spacecraft interior. The Astrobee free-flying robots that operate inside the International Space Station (ISS) collected the datasets. Space intra-vehicular free-flyers face unique localization challenges: their IMU does not provide a gravity vector, their attitude is fully arbitrary, and they operate in a dynamic, cluttered environment. We extensively evaluate state-of-the-art visual navigation algorithms on these challenging Astrobee datasets, showing superior performance of classical geometry-based methods over recent data-driven approaches. The datasets include monocular images and IMU measurements, with multiple sequences performing a variety of maneuvers and covering four ISS modules. The sensor data is spatio-temporally aligned, and extrinsic/intrinsic calibrations, ground-truth 6-DoF camera poses, and detailed 3D CAD models are included to support evaluation. The datasets are available at: https://astrobee-iss-dataset.github. io/.
Association between Statins Administration and Influenza Susceptibility: A Systematic Review and Meta-Analysis of Longitudinal Studies

Previous studies reported that the association between statins use and influenza infection was contradictory. A systematic review and meta-analysis of longitudinal studies were performed to determine the association between statins use and influenza susceptibility. The literature search was conducted in PubMed, Embase, and Web of Science, from each database’s inception to 21 May 2023. The fixed effect model and random effects model were used for data synthesis. In our study, a total of 1,472,239 statins users and 1,486,881 statins non-users from five articles were included. The pooled risk ratio (RR) of all included participants was 1.05 (95% CI: 1.03–1.07), and there were still significant differences after adjusting for vaccination status. Of note, RR values in statins users were 1.06 (95% CI: 1.03–1.08) in people aged ≥60 years old and 1.05 (95% CI: 1.03–1.07) in participant groups with a higher proportion of females. Administration of statins might be associated with an increased risk of influenza infection, especially among females and elderly people. For those people using statins, we should pay more attention to surveillance of their health conditions and take measures to prevent influenza infection.
A Comprehensive Review of Muscle–Tendon Junction: Structure, Function, Injury and Repair

The muscle–tendon junction (MTJ) is a highly specific tissue interface where the muscle’s fascia intersects with the extracellular matrix of the tendon. The MTJ functions as the particular structure facilitating the transmission of force from contractive muscle fibers to the skeletal system, enabling movement. Considering that the MTJ is continuously exposed to constant mechanical forces during physical activity, it is susceptible to injuries. Ruptures at the MTJ often accompany damage to both tendon and muscle tissues. In this review, we attempt to provide a precise definition of the MTJ, describe its subtle structure in detail, and introduce therapeutic approaches related to MTJ tissue engineering. We hope that our detailed illustration of the MTJ and summary of the representative research achievements will help researchers gain a deeper understanding of the MTJ and inspire fresh insights and breakthroughs for future research.