Miracle-dz/UniFAR-FTUs huggingface dataset:
Minkowski Functionals of Large-Scale Structure as a Probe of Modified Gravity

In this study, we explore the potential of utilizing the four Minkowski functionals, which can fully describe the morphological properties of the large-scale structures, as a robust tool for investigating the modified gravity, particularly on non-linear and quasi-linear scales. With the assistance of the N-body simulation, we employ the Minkowski functionals to probe the Hu-Sawicki f(R) gravity model. The focus is on understanding the morphorlogical properties extracted by the Minkowski functionals and their sensitivity to modified gravity. Our analysis involves a comprehensive examination of the cosmic variance arising from finite simulation volumes. By systematically varying smoothing scales and redshifts, we quantify the information encoded in the Minkowski functionals measured from the dark-matter density field. The goal is to assess the capacity of the Minkowksi functionals to constrain the model and explore potential improvements through their combination. Additionally, we investigate the impact of using biased tracers such as dark matter halos and the halo occupation distribution galaxies on the modified gravity signatures within the Minkowksi functionals of the LSS. Furthermore, we evaluate the influence of the redshift space distortion on the observed results. In summary, our study suggests that the Minkowski functionals of the large-scale structures hold promise as a stringent tool for constraining modified gravity and offer valuable insights into the morphological features of the cosmic web.
A Sea-Land Clutter Classification Framework for Over-the-Horizon-Radar Based on Weighted Loss Semi-supervised GAN

Deep convolutional neural network has made great achievements in sea-land clutter classification for over-the-horizon-radar (OTHR). The premise is that a large number of labeled training samples must be provided for a sea-land clutter classifier. In practical engineering applications, it is relatively easy to obtain label-free sea-land clutter samples. However, the labeling process is extremely cumbersome and requires expertise in the field of OTHR. To solve this problem, we propose an improved generative adversarial network, namely weighted loss semi-supervised generative adversarial network (WL-SSGAN). Specifically, we propose a joint feature matching loss by weighting the middle layer features of the discriminator of semi-supervised generative adversarial network. Furthermore, we propose the weighted loss of WL-SSGAN by linearly weighting standard adversarial loss and joint feature matching loss. The semi-supervised classification performance of WL-SSGAN is evaluated on a sea-land clutter dataset. The experimental results show that WL-SSGAN can improve the performance of the fully supervised classifier with only a small number of labeled samples by utilizing a large number of unlabeled sea-land clutter samples. Further, the proposed weighted loss is superior to both the adversarial loss and the feature matching loss. Additionally, we compare WL-SSGAN with conventional semi-supervised classification methods and demonstrate that WL-SSGAN achieves the highest classification accuracy.
Generalizations of Bertrand’s Postulate to Sums of Any Number of Primes

Summary In 1845, Bertrand conjectured what became known as Bertrand’s postulate or the Bertrand-Chebyshev theorem: twice and prime strictly exceeds the next prime. Surprisingly, a stronger statement seems not to be well-known: the sum of any two consecutive primes strictly exceeds the next prime, except for the only equality . Our main theorem is a much more general result, perhaps not previously noticed, that compares sums of any number of primes. We prove this result using only the prime number theorem. We also give some numerical results and unanswered questions.
Random Tur\'an theorem for expansions of spanning subgraphs of tight trees

The $r$-expansion of a $k$-uniform hypergraph $H$, denoted by $H^{(+r)}$, is an $r$-uniform hypergraph obtained by enlarging each $k$-edge of $H$ with a set of $r-k$ vertices of degree one. The random Tur\'an number $\mathrm{ex}(G^r_{n,p},H)$ is the maximum number of edges in an $H$-free subgraph of $G^r_{n,p}$, where $G^r_{n,p}$ is the Erd\"os-R\'enyi random $r$-graph with parameter $p$. In this paper, we prove an upper bound for $\mathrm{ex}(G^r_{n,p},H)$ when $H$ belongs to a large family of $r$-partite $r$-graphs: the $r$-expansion of spanning subgraphs of tight trees. This upper bound is essentially tight for at least the following two families of hypergraphs. 1. Let $T$ be a tight tree such that the intersection of all edges of $T$ is empty. Simple construction shows that the upper bound we have for expansions of $T$ is essentially tight. 2. We find a non-trivial and essentially tight lower bound for expansions of $K^k_{k+1}$, the complete $k$-graph on $k+1$ vertices. The proof of the lower bound makes use of a recent construction of Gowers and Janzer generalizing the famous Ruzsa-Szemer\"edi construction. This result answers a question of the current author, Spiro and Verstra\"ete concerning $\mathrm{ex}(G^r_{n,p},K^{(+r)}_3)$, the random Tur\'an number of linear triangle.
Correlates of repeat abortions and their spacing: Evidence from registry data in Spain

Using administrative data on all induced abortions recorded in Spain in 2019, we analyze the characteristics of women undergoing repeat abortions and the spacing between these procedures. Our findings indicate that compared to women experiencing their first abortion, those who undergo repeat abortions are more likely to have lower education levels, have dependent children, live alone, or be foreign-born, with a non-monotonic relationship with age. We also report that being less educated, not employed, having dependent children, or being foreign-born are all strongly related to a higher number of repeat abortions. Lastly, we find that being less educated, foreign-born, or not employed is correlated with a shorter time interval between the last two abortions.
Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Background: The segment-anything model (SAM), introduced in April 2023, shows promise as a benchmark model and a universal solution to segment various natural images. It comes without previously-required re-training or fine-tuning specific to each new dataset. Purpose: To test SAM's accuracy in various medical image segmentation tasks and investigate potential factors that may affect its accuracy in medical images. Methods: SAM was tested on 12 public medical image segmentation datasets involving 7,451 subjects. The accuracy was measured by the Dice overlap between the algorithm-segmented and ground-truth masks. SAM was compared with five state-of-the-art algorithms specifically designed for medical image segmentation tasks. Associations of SAM's accuracy with six factors were computed, independently and jointly, including segmentation difficulties as measured by segmentation ability score and by Dice overlap in U-Net, image dimension, size of the target region, image modality, and contrast. Results: The Dice overlaps from SAM were significantly lower than the five medical-image-based algorithms in all 12 medical image segmentation datasets, by a margin of 0.1-0.5 and even 0.6-0.7 Dice. SAM-Semantic was significantly associated with medical image segmentation difficulty and the image modality, and SAM-Point and SAM-Box were significantly associated with image segmentation difficulty, image dimension, target region size, and target-vs-background contrast. All these 3 variations of SAM were more accurate in 2D medical images, larger target region sizes, easier cases with a higher Segmentation Ability score and higher U-Net Dice, and higher foreground-background contrast.
Generalization Guarantees for Multi-Item Profit Maximization: Pricing, Auctions, and Randomized Mechanisms

Sample Complexity of Multi-Item Profit Maximization Historically, mechanism design has rested on the assumption that the seller knows the distribution over buyers’ values. In practice, a description of this distribution is typically unavailable. In “Generalization Guarantees for Multi-Item Profit Maximization: Pricing, Auctions, and Randomized Mechanisms,” we assume only sample access to this distribution. When using samples to optimize over a complex mechanism class—such as the set of all multi-item, multibuyer mechanisms—a mechanism may have high average profit over the samples, but low expected profit. This raises the question: How many samples are sufficient to ensure that a mechanism’s average profit is close to its expected profit? To answer this question, we uncover structure shared across many mechanisms: Profit is piecewise linear in the mechanism’s parameters for any set of buyers’ values. We prove new bounds for mechanism classes not yet studied in the sample-based mechanism design literature and match or improve over best-known guarantees for many classes.
Beyond the Standard Model Physics in the Far-forward Region of the Large Hadron Collider

A new physics program has been initiated as part of the ongoing LHC physics run in the far-forward region, where dedicated FASER and SND@LHC experiments are currently taking data. We discuss the possible discovery prospects of this program in the search for signatures of beyond the Standard Model physics. We focus on both the present period and the proposed future Forward Physics Facility (FPF) that will operate in the high luminosity LHC era.
Improving Real-Time Bidding in Online Advertising Using Markov Decision Processes and Machine Learning Techniques

Real-time bidding has emerged as an effective online advertising technique. With real-time bidding, advertisers can position ads per impression, enabling them to optimise ad campaigns by targeting specific audiences in real-time. This paper proposes a novel method for real-time bidding that combines deep learning and reinforcement learning techniques to enhance the efficiency and precision of the bidding process. In particular, the proposed method employs a deep neural network to predict auction details and market prices and a reinforcement learning algorithm to determine the optimal bid price. The model is trained using historical data from the i Pin You dataset and compared to cutting-edge real-time bidding algorithms. The outcomes demonstrate that the proposed method is preferable regarding cost-effectiveness and precision. In addition, the study investigates the influence of various model parameters on the performance of the proposed algorithm. It offers insights into the efficacy of the combined deep learning and reinforcement learning approach for real-time bidding. This study contributes to advancing techniques and offers a promising direction for future research.
Computationally modelling cell proliferation: A review

Cell proliferation is vital for the development and homeostasis of the human body. For such to occur, cells go through the cell cycle during which they replicate their genetic material and ultimately complete cellular division, when one cell divides into two new cells with equal genetic material. However, if there are some errors or abnormalities during the cell cycle that disrupt the balance between cell death and proliferation, severe problems can occur, such as tumour development, which is currently one of the leading causes of death in the world. Nowadays, mathematical and computational models are used to understand and study several biological mechanisms and processes, namely cellular proliferation. Over the last forty‐five years, several models have attempted to describe cell proliferation and its regulation. Due to the complexity of the process, numerous assumptions and simplifications have been considered. This work presents a review of some of these models, focusing mainly on mammalian or generic eukaryotic models. Previously published continuum, discrete and hybrid approaches are presented and compared, in order to understand and highlight the relevance and capabilities of these models, their shortcomings and future challenges.
Gene expression profiling and multiplex immunofluorescence analysis of bullous pemphigoid immune‐related adverse event reveal upregulation of toll‐like receptor 4/complement‐induced innate immune response and increased density of TH1 T‐cells

Immune checkpoint inhibitor (ICI)‐based cancer therapies cause a variety of cutaneous immune‐related adverse events (irAEs) including immunobullous skin eruptions like bullous pemphigoid (BP). However, little is known about the underlying immunopathogenic drivers of these reactions, and understanding the unique gene expression profile and immune composition of BP‐irAE remains a critical knowledge gap in the field of oncodermatology/oncodermatopathology.
Effective Factors in the Implementation 0 f ELT Curriculum Innovations

This paper highlights the complex process of the implementation of ELT curriculum innovations. It also confirms that teachers are not simply implementers of policies that are handed down to them, but they interpret, modify, alter, and implement these policies according to their beliefs and the context where these policies are being implemented. In addition, this paper, illustrates a number of factors which influence how teachers implement and make sense of ELT curriculum innovations. This paper provides significant implications and useful messages for curriculum developers, teachers’ education programs, and educational policy makers.
Iron Delivery through Membrane Vesicles in Corynebacterium glutamicum

Iron is an essential element of life. Many bacteria have developed iron acquisition systems, such as siderophores, for external iron uptake. Corynebacterium glutamicum, a soil bacterium known for its potential for industrial applications, was shown to lack the ability to produce extracellular, low-molecular-weight iron carriers, and it remains elusive how this bacterium acquires iron. ABSTRACT Bacterial cells form and release membrane vesicles (MVs) originating from cellular membranes. In recent years, many biological functions of bacterial MVs have been identified. Here, we show that MVs derived from Corynebacterium glutamicum, a model organism for mycolic acid-containing bacteria, can mediate iron acquisition and other phylogenetically related bacteria. Lipid/protein analysis and iron quantification assay indicate that C. glutamicum MVs formed by outer mycomembrane blebbing can load ferric iron (Fe3+) as its cargo. Iron-loaded C. glutamicum MVs promoted the growth of producer bacteria in iron-limited liquid media. MVs were received by C. glutamicum cells, suggesting a direct transfer of iron to the recipient cells. Cross-feeding of C. glutamicum MVs with phylogenetically close (Mycobacterium smegmatis and Rhodococcus erythropolis) or distant (Bacillus subtilis) bacteria indicated that C. glutamicum MVs could be received by the different species tested, while iron uptake is limited to M. smegmatis and R. erythropolis. In addition, our results indicate that iron loading on MVs in C. glutamicum does not depend on membrane-associated proteins or siderophores, which is different from what has been shown in other mycobacterial species. Our findings illustrate the biological importance of MV-associated extracellular iron for C. glutamicum growth and suggest its ecological impact on selected members of microbial communities. IMPORTANCE Iron is an essential element of life. Many bacteria have developed iron acquisition systems, such as siderophores, for external iron uptake. Corynebacterium glutamicum, a soil bacterium known for its potential for industrial applications, was shown to lack the ability to produce extracellular, low-molecular-weight iron carriers, and it remains elusive how this bacterium acquires iron. Here, we demonstrated that MVs released from C. glutamicum cells could act as extracellular iron carriers that mediate iron uptake. Although MV-associated proteins or siderophores have been shown to play critical roles in MV-mediated iron uptake by other mycobacterial species, the iron delivery through C. glutamicum MVs is not dependent on these factors. Moreover, our results suggest that there is an unidentified mechanism that determines the species specificity of MV-mediated iron acquisition. Our results further demonstrated the important role of MV-associated iron.
Single-Cell, High-Content Microscopy Analysis of BK Polyomavirus Infection

BK polyomavirus (BKPyV) is a human pathogen that infects nearly everyone by adulthood and persists throughout a person’s life. Only people with significant immune suppression develop disease from the virus, however. ABSTRACT By adulthood, the majority of the population is persistently infected with BK polyomavirus (BKPyV). Only a subset of the population, generally transplant recipients on immunosuppressive drugs, will experience disease from BKPyV, but those who do have few treatment options and, frequently, poor outcomes, because to date there are no effective antivirals to treat or approved vaccines to prevent BKPyV. Most studies of BKPyV have been performed on bulk populations of cells, and the dynamics of infection at single-cell resolution have not been explored. As a result, much of our knowledge is based upon the assumption that all cells within a greater population are behaving the same way with respect to infection. The present study examines BKPyV infection on a single-cell level using high-content microscopy to measure and analyze the viral protein large T antigen (TAg), promyelocytic leukemia protein (PML), DNA, and nuclear morphological features. We observed significant heterogeneity among infected cells, within and across time points. We found that the levels of TAg within individual cells did not necessarily increase with time and that cells with the same TAg levels varied in other ways. Overall, high-content, single-cell microscopy is a novel approach to studying BKPyV that enables experimental insight into the heterogenous nature of the infection. IMPORTANCE BK polyomavirus (BKPyV) is a human pathogen that infects nearly everyone by adulthood and persists throughout a person’s life. Only people with significant immune suppression develop disease from the virus, however. Until recently the only practical means of studying many viral infections was to infect a group of cells in the laboratory and measure the outcomes in that group. However, interpreting these bulk population experiments requires the assumption that infection influences all cells within a group similarly. This assumption has not held for multiple viruses tested so far. Our study establishes a novel single-cell microscopy assay for BKPyV infection. Using this assay, we discovered differences among individual infected cells that have not been apparent in bulk population studies. The knowledge gained in this study and the potential for future use demonstrate the power of this assay as a tool for understanding the biology of BKPyV.
Harpin enhances antioxidant nutrient accumulation and decreases enzymatic browning in stored soybean sprouts

Abstract Enzymatic browning causes quality losses in the soybean sprout industry. Herein, the effects of harpin, a proteinaceous bacterial elicitor isolated from Erwinia amylovora, in regulating enzymatic browning and antioxidant nutrient accumulation in stored soybean sprout were investigated. Enhanced soybean sprout growth (evaluated by sprout length and fresh weight) occurred after spraying three times (0, 2, and 4 days after sowing) with 30 mg L−1 harpin during the growth stage. The decline in vitamin C and total phenolic contents and total antioxidant capacity (evaluated by Fe3+ reducing power) were attenuated by harpin during storage. Harpin increased phenylalanine ammonia-lyase, NADPH oxidase (NOX), superoxide dismutase, and catalase activities and inhibited polyphenol oxidase activity and enzymatic browning in soybean sprouts during storage. However, this harpin-promoted sprout growth, enhanced antioxidant accumulation and enzyme activity and improved sprout quality could be partly, but drastically, abolished using diphenyleneiodonium chloride, a specific inhibitor of NOX. Moreover, the mechanisms by which harpin influences antioxidant accumulation and enzymatic browning in soybean sprouts during storage were discussed from the perspective of NOX-mediated H2O2 signalling. Graphical abstract
A review of the use of giant reed (Arundo donax L.) in the biorefineries context

Abstract The massive availability of biomass generated by the common giant reed (Arundo donax L.) motivates the search for its possible industrial use for the generation of high added-value products through implementing a biorefinery approach. The literature demonstrates the potential of common cane to obtain different high-value compounds, such as levulinic acid, oligosaccharides, fermentable sugars, highly digestible fiber for animal feed, polyphenols, and natural fibers for composite materials, among others. The data shows the upward trend in Europe toward the generation of new green industries, grouped under the biorefinery concept. Therefore, this review summarizes the current knowledge on the use of Arundo to produce materials, fibers, and chemicals. Major environmental concerns related to this plant are also reviewed. Special attention has been paid to the potential use of Arundo to produce chemicals using green chemistry approaches, as a way to contribute to and advance the achievement of Sustainable Development Goals. Recommendations for future research are also outlined.
End-of-life Care in the Intesive Care Unit and Nursing Roles in Communicating with Families

ABSTRACT Introduction Professionals in Intensive Care Units face death, shifting their role from therapists to caregivers in end-of-life management. The nursing attitude and response to death has been shown to affect the quality of palliative care and end-of-life services that are interrelated services. Aim of the study The aim of this research was to evaluate the professional attitude of nurses towards the care of the families of critically ill patients in the ICU, leading to the emergence of specific attitudes, relating them to their demographic and professional characteristics, with the aim of drawing conclusions for the improvement of quality in end-of-life care. Material and Methods The sample of the study was 81 nurses from a large tertiary hospital. Participants completed the “Nurse Activities for Communicating with Families” (NACF) questionnaire. The questions are about ways in which nurses can help the patient’s family during the patient’s stay in the ICU. Results The results revealed that the nurses took actions related to the information and psychological support of the patient’s family. On the contrary, they did not focus on the spiritual / religious needs of the patient and the needs of the family based on their cultural background. Conclusions The professional treatment of staff is characterized by compassion and empathy, but it is necessary to train them on important issues related to diversity, including the religious, spiritual values and beliefs of patients and their relatives.