Miracle-dz/UniFAR-FTUs huggingface dataset:
3D bioprinting: opportunities for wound dressing development

The skin is the body’s first line of defence, and its physiology is complex. When injury occurs, the skin goes through a complex recovery process, and there is the risk of developing a chronic wound. Therefore, proper wound care is critical during the healing process. In response to clinical needs, wound dressings have been developed. There are several types of wound dressings available for wound healing, but there are still many issues to overcome. With its high controllability and resolution, three-dimensional (3D) printing technology is widely regarded as the technology of the next global industrial and manufacturing revolution, and it is a key driving force in the development of wound dressings. Here, we briefly introduce the wound healing mechanism, organize the history and the main technologies of 3D bioprinting, and discuss the application as well as the future direction of development of 3D bioprinting technology in the field of wound dressings.
A Novel Feature Engineering Method Based on Latent Representation Learning for Radiomics: Application in NSCLC Subtype Classification

Radiomics refers to the high-throughput extraction of quantitative features from medical images, and is widely used to construct machine learning models for the prediction of clinical outcomes, while feature engineering is the most important work in radiomics. However, current feature engineering methods fail to fully and effectively utilize the heterogeneity of features when dealing with different kinds of radiomics features. In this work, latent representation learning is first presented as a novel feature engineering approach to reconstruct a set of latent space features from original shape, intensity and texture features. This proposed method projects features into a subspace called latent space, in which the latent space features are obtained by minimizing a unique hybrid loss function including a clustering-like loss and a reconstruction loss. The former one ensures the separability among each class while the latter one narrows the gap between the original features and latent space features. Experiments were performed on a multi-center non-small cell lung cancer (NSCLC) subtype classification dataset from 8 international open databases. Results showed that compared with four traditional feature engineering methods (baseline, PCA, Lasso and L2,1-norm minimization), latent representation learning could significantly improve the classification performance of various machine learning classifiers on the independent test set (all p < 0.001). Further on two additional test sets, latent representation learning also showed a significant improvement in generalization performance. Our research shows that latent representation learning is a more effective feature engineering method, which has the potential to be used as a general technology in a wide range of radiomics researches.
Genetics: A Starting Point for the Prevention and the Treatment of Obesity

Obesity is a common, serious, and costly disease. More than 1 billion people worldwide are obese—650 million adults, 340 million adolescents, and 39 million children. The WHO estimates that, by 2025, approximately 167 million people—adults and children—will become less healthy because they are overweight or obese. Obesity-related conditions include heart disease, stroke, type 2 diabetes, and certain types of cancer. These are among the leading causes of preventable, premature death. The estimated annual medical cost of obesity in the United States was nearly $173 billion in 2019 dollars. Obesity is considered the result of a complex interaction between genes and the environment. Both genes and the environment change in different populations. In fact, the prevalence changes as the result of eating habits, lifestyle, and expression of genes coding for factors involved in the regulation of body weight, food intake, and satiety. Expression of these genes involves different epigenetic processes, such as DNA methylation, histone modification, or non-coding micro-RNA synthesis, as well as variations in the gene sequence, which results in functional alterations. Evolutionary and non-evolutionary (i.e., genetic drift, migration, and founder’s effect) factors have shaped the genetic predisposition or protection from obesity in modern human populations. Understanding and knowing the pathogenesis of obesity will lead to prevention and treatment strategies not only for obesity, but also for other related diseases.
Face Recognition Method Based on Principal Component Analysis

PCA(Principal Component Analysis) is the most used method in human face recognition.PCA algorithm can get the linear transformation matrix which transform a high-dimensional to low-dimensional space,can approximate the original data with lower dimension feature vector.This transformation matrix can be used to obtain a subspace(eigenfaces). When identifying,the face is projected to the subspace,using nearest neighbor method has been a recent point in order to identify the identity of the person.
Subviral Dense Bodies of Human Cytomegalovirus Enhance Interferon-Beta Responses in Infected Cells and Impair Progeny Production

(1) Background: Infection with human cytomegalovirus (HCMV) leads to the production and release of subviral particles, termed Dense Bodies (DB). They are enclosed by a membrane resembling the viral envelope. This membrane mediates the entrance of DBs into cells in a way that is comparable to virus infection. HCMV attachment and entry trigger the induction of interferon synthesis and secretion, and the subsequent expression of interferon-regulated genes (IRGs) that might inhibit replication of the virus. Recently, we demonstrated that DBs induce a robust interferon response in the absence of infection. Little is known thus far, including how DBs influence HCMV infection and virus–host interaction. (2) Methods: Purified DBs were used to study the impact on virus replication and on the innate defense mechanisms of the cell. (3) Results: The incubation of cells with DBs at the time of infection had little effect on viral genome replication. Preincubation of DBs, however, led to a marked reduction in viral release from infected cells. These cells showed an enhancement of the cytopathic effect, associated with a moderate increase in early apoptosis. Despite virus-induced mechanisms to limit the interferon response, the induction of interferon-regulated genes (IRGs) was upregulated by DB treatment. (4) Conclusions: DBs sensitize cells against viral infection, comparable to the effects of interferons. The activities of these particles need to be considered when studying viral–host interaction.
Remote sensing of assimilating branch light use efficiency using the photochemical reflectance index in Haloxylon ammodendron forest

Abstract. Five healthy Haloxylon ammodendron trees were randomly selected to monitor the photochemical reflectance index (PRI) and its relation to light use efficiency (LUE) in shaded and sunlit assimilating branches. Linear regression was performed where the results revealed that LUE decreased with seasonal courses with vivid midday depression regardless of light variation within canopies. PRI and LUE decoupled in early spring when the foliage was deeply downregulated, with high correlation (R2=0.8, p<0.001) during most parts of the year. Measured and estimated LUE were highly correlated for the July to September dataset (R2=0.73, p<0.001) compared to May to June datasets (R2=0.35, p<0.001). The estimated LUE for young assimilating branches could be obtained with a high RMSE of 0.01, compared to mature branches with an RMSE of 0.003. The measured LUE values were ∼20% higher than estimated LUE during the period from May to June, and values were slightly lower at the end of the growing season. This was attributed to differences in the physiological and biochemical mechanisms controlling the seasonal dynamics of PRI. Therefore, PRI is the best estimator of LUE when age-related changes and canopy light partitioning are considered in assimilating organs. Combined use of PRI with pigments could improve the remote evaluation of LUE.
Akkermansia muciniphila alleviates high‐fat‐diet‐related metabolic‐associated fatty liver disease by modulating gut microbiota and bile acids

It has been reported that Akkermansia muciniphila improves host metabolism and reduces inflammation; however, its potential effects on bile acid metabolism and metabolic patterns in metabolic‐associated fatty liver disease (MAFLD) are unknown. In this study, we have analysed C57BL/6 mice under three feeding conditions: (i) a low‐fat diet group (LP), (ii) a high‐fat diet group (HP) and (iii) a high‐fat diet group supplemented with A. muciniphila (HA). The results found that A. muciniphila administration relieved weight gain, hepatic steatosis and liver injury induced by the high‐fat diet. A. muciniphila altered the gut microbiota with a decrease in Alistipes, Lactobacilli, Tyzzerella, Butyricimonas and Blautia, and an enrichment of Ruminiclostridium, Osclibacter, Allobaculum, Anaeroplasma and Rikenella. The gut microbiota changes correlated significantly with bile acids. Meanwhile, A. muciniphila also improved glucose tolerance, gut barriers and adipokines dysbiosis. Akkermansia muciniphila regulated the intestinal FXR‐FGF15 axis and reshaped the construction of bile acids, with reduced secondary bile acids in the caecum and liver, including DCA and LCA. These findings provide new insights into the relationships between probiotics, microflora and metabolic disorders, highlighting the potential role of A. muciniphila in the management of MAFLD.
Magnetic Nanoparticle-Based High-Performance Positive and Negative Magnetic Resonance Imaging Contrast Agents

In recent decades, magnetic nanoparticles (MNPs) have attracted considerable research interest as versatile substances for various biomedical applications, particularly as contrast agents in magnetic resonance imaging (MRI). Depending on their composition and particle size, most MNPs are either paramagnetic or superparamagnetic. The unique, advanced magnetic properties of MNPs, such as appreciable paramagnetic or strong superparamagnetic moments at room temperature, along with their large surface area, easy surface functionalization, and the ability to offer stronger contrast enhancements in MRI, make them superior to molecular MRI contrast agents. As a result, MNPs are promising candidates for various diagnostic and therapeutic applications. They can function as either positive (T1) or negative (T2) MRI contrast agents, producing brighter or darker MR images, respectively. In addition, they can function as dual-modal T1 and T2 MRI contrast agents, producing either brighter or darker MR images, depending on the operational mode. It is essential that the MNPs are grafted with hydrophilic and biocompatible ligands to maintain their nontoxicity and colloidal stability in aqueous media. The colloidal stability of MNPs is critical in order to achieve a high-performance MRI function. Most of the MNP-based MRI contrast agents reported in the literature are still in the developmental stage. With continuous progress being made in the detailed scientific research on them, their use in clinical settings may be realized in the future. In this study, we present an overview of the recent developments in the various types of MNP-based MRI contrast agents and their in vivo applications.
Lignin Nanoparticles with Entrapped Thymus spp. Essential Oils for the Control of Wood-Rot Fungi

After decades of utilization of fossil-based and environmentally hazardous compounds for wood preservation against fungal attack, there is a strong need to substitute those compounds with bio-based bioactive solutions, such as essential oils. In this work, lignin nanoparticles containing four essential oils from thyme species (Thymus capitatus, Coridothymus capitatus, T. vulgaris, and T. vulgaris Demeter) were applied as biocides in in vitro experiments to test their anti-fungal effect against two white-rot fungi (Trametes versicolor and Pleurotus ostreatus) and two brown-rot fungi (Poria monticola and Gloeophyllum trabeum). Entrapment of essential oils provided a delayed release over a time frame of 7 days from the lignin carrier matrix and resulted in lower minimum inhibitory concentrations of the essential oils against the brown-rot fungi (0.30–0.60 mg/mL), while for the white-rot fungi, identical concentrations were determined compared with free essential oils (0.05–0.30 mg/mL). Fourier Transform infrared (FTIR) spectroscopy was used to assess the fungal cell wall changes in the presence of essential oils in the growth medium. The results regarding brown-rot fungi present a promising approach for a more effective and sustainable utilization of essential oils against this class of wood-rot fungi. In the case of white-rot fungi, lignin nanoparticles, as essential oils delivery vehicles, still need optimization in their efficacy.
Combination of Panax ginseng and Diospyros kaki Leaf Inhibits White Adipocyte Differentiation and Browning Process through AMP-Activated Protein Kinase (AMPK) Activation In Vitro and In Vivo

Activating brown adipose tissue (BAT) and stimulating white adipose tissue (WAT) browning is a prospective obesity treatment method. Dietary components derived from plants are the most effective approach to activate BAT and promote WAT browning in rodents. This study investigated the synergistic effects of Panax ginseng (PG) and Diospyros kaki leaf (DKL) extract on adipocyte differentiation and browning, as well as the molecular mechanism underlying their beneficial effects. The administration of PG and DKL to HFD-induced obese mice significantly decreased body weight and epididymal and abdominal adipose tissue mass. In in vitro, PG inhibited the adipogenesis of 3T3-L1 adipocytes by regulating the expression of key adipogenic regulators, such as peroxisome proliferator-activated receptor (PPAR)γ and CCAAT/enhancer-binding protein (C/EBP)-α. In contrast, DKL negligibly influenced the adipogenesis of 3T3-L1 adipocytes but greatly increased the protein expression of UCP-1, PGC-1α, and PPARα in BAT and/or WAT. Moreover, PG and DKL inhibited adipogenesis synergistically and activated white adipocyte browning via AMP-activated protein kinase (AMPK) and sirtuin 1 (SIRT1) pathways. These results suggest that a combination of PG and DKL regulates adipogenesis in white adipocytes and browning in brown adipocytes by activating AMPK/SIRT1 axis. The potential use of PG and DKL may represent an important strategy in obesity management that will be safer and more effective.
First Detection of Hepatitis E Virus (Rocahepevirus ratti Genotype C1) in Synanthropic Norway Rats (Rattus norvegicus) in Romania

Hepatitis E virus (HEV) is an emerging zoonotic pathogen with different viral genera and species reported in a wide range of animals. Rodents, particularly rats, carry the specific genus rat HEV (Rocahepevirus genus, genotype C1) and are exposed occasionally to HEV-3 (Paslahepevirus genus, genotype 3), a zoonotic genotype identified in humans and widely distributed in domestic and feral pigs. In this study, the presence of HEV was investigated in synanthropic Norway rats from Eastern Romania, in areas where the presence of HEV-3 was previously reported in pigs, wild boars and humans. Using methods capable of detecting different HEV species, the presence of HEV RNA was investigated in 69 liver samples collected from 52 rats and other animal species. Nine rat liver samples were identified as being positive for rat HEV RNA (17.3%). High sequence identity (85–89% nt) was found with other European Rocahepevirus. All samples tested from other animal species, within the same environment, were negative for HEV. This is the first study to demonstrate the presence of HEV in rats from Romania. Since rat HEV has been reported to cause zoonotic infections in humans, this finding supports the need to extend the diagnosis of Rocahepevirus in humans with suspicion of hepatitis.
Highly Sensitive Detection of Bacteria by Binder-Coupled Multifunctional Polymeric Dyes

Infectious diseases caused by pathogens are a health burden, but traditional pathogen identification methods are complex and time-consuming. In this work, we have developed well-defined, multifunctional copolymers with rhodamine B dye synthesized by atom transfer radical polymerization (ATRP) using fully oxygen-tolerant photoredox/copper dual catalysis. ATRP enabled the efficient synthesis of copolymers with multiple fluorescent dyes from a biotin-functionalized initiator. Biotinylated dye copolymers were conjugated to antibody (Ab) or cell-wall binding domain (CBD), resulting in a highly fluorescent polymeric dye-binder complex. We showed that the unique combination of multifunctional polymeric dyes and strain-specific Ab or CBD exhibited both enhanced fluorescence and target selectivity for bioimaging of Staphylococcus aureus by flow cytometry and confocal microscopy. The ATRP-derived polymeric dyes have the potential as biosensors for the detection of target DNA, protein, or bacteria, as well as bioimaging.
Towards a Better Understanding of Learning with Multiagent Teams

While it has long been recognized that a team of individual learning agents can be greater than the sum of its parts, recent work has shown that larger teams are not necessarily more effective than smaller ones. In this paper, we study why and under which conditions certain team structures promote effective learning for a population of individual learning agents. We show that, depending on the environment, some team structures help agents learn to specialize into specific roles, resulting in more favorable global results. However, large teams create credit assignment challenges that reduce coordination, leading to large teams performing poorly compared to smaller ones. We support our conclusions with both theoretical analysis and empirical results.
Solving the Identifying Code Set Problem with Grouped Independent Support

An important problem in network science is finding an optimal placement of sensors in nodes in order to uniquely detect failures in the network. This problem can be modelled as an identifying code set (ICS) problem, introduced by Karpovsky et al. in 1998. The ICS problem aims to find a cover of a set S, such that the elements in the cover define a unique signature for each of the elements of S, and to minimise the cover’s cardinality. In this work, we study a generalised identifying code set (GICS) problem, where a unique signature must be found for each subset of S that has a cardinality of at most k (instead of just each element of S). The concept of an independent support of a Boolean formula was introduced by Chakraborty et al. in 2014 to speed up propositional model counting, by identifying a subset of variables whose truth assignments uniquely define those of the other variables. In this work, we introduce an extended version of independent support, grouped independent support (GIS), and show how to reduce the GICS problem to the GIS problem. We then propose a new solving method for finding a GICS, based on finding a GIS. We show that the prior state-of-the-art approaches yield integer-linear programming (ILP) models whose sizes grow exponentially with the problem size and k, while our GIS encoding only grows polynomially with the problem size and k. While the ILP approach can solve the GICS problem on networks of at most 494 nodes, the GIS-based method can handle networks of up to 21 363 nodes; a ∼40× improvement. The GIS-based method shows up to a 520× improvement on the ILP-based method in terms of median solving time. For the majority of the instances that can be encoded and solved by both methods, the cardinality of the solution returned by the GIS-based method is less than 10% larger than the cardinality of the solution found by the ILP method.
Emulating the dynamics of complex systems using autoregressive models on manifolds (mNARX)

We propose a novel surrogate modelling approach to efficiently and accurately approximate the response of complex dynamical systems driven by time-varying exogenous excitations over extended time periods. Our approach, namely manifold nonlinear autoregressive modelling with exogenous input (mNARX), involves constructing a problem-specific exogenous input manifold that is optimal for constructing autoregressive surrogates. The manifold, which forms the core of mNARX, is constructed incrementally by incorporating the physics of the system, as well as prior expert- and domain- knowledge. Because mNARX decomposes the full problem into a series of smaller sub-problems, each with a lower complexity than the original, it scales well with the complexity of the problem, both in terms of training and evaluation costs of the final surrogate. Furthermore, mNARX synergizes well with traditional dimensionality reduction techniques, making it highly suitable for modelling dynamical systems with high-dimensional exogenous inputs, a class of problems that is typically challenging to solve. Since domain knowledge is particularly abundant in physical systems, such as those found in civil and mechanical engineering, mNARX is well suited for these applications. We demonstrate that mNARX outperforms traditional autoregressive surrogates in predicting the response of a classical coupled spring-mass system excited by a one-dimensional random excitation. Additionally, we show that mNARX is well suited for emulating very high-dimensional time- and state-dependent systems, even when affected by active controllers, by surrogating the dynamics of a realistic aero-servo-elastic onshore wind turbine simulator. In general, our results demonstrate that mNARX offers promising prospects for modelling complex dynamical systems, in terms of accuracy and efficiency.
Deep Unfolded Simulated Bifurcation for Massive MIMO Signal Detection

Multiple-input multiple-output (MIMO) is a key ingredient of next-generation wireless communications. Recently, various MIMO signal detectors based on deep learning techniques and quantum(-inspired) algorithms have been proposed to improve the detection performance compared with conventional detectors. This paper focuses on the simulated bifurcation (SB) algorithm, a quantum-inspired algorithm. This paper proposes two techniques to improve its detection performance. The first is modifying the algorithm inspired by the Levenberg-Marquardt algorithm to eliminate local minima of maximum likelihood detection. The second is the use of deep unfolding, a deep learning technique to train the internal parameters of an iterative algorithm. We propose a deep-unfolded SB by making the update rule of SB differentiable. The numerical results show that these proposed detectors significantly improve the signal detection performance in massive MIMO systems.
Reconstructing the Hemodynamic Response Function via a Bimodal Transformer

The relationship between blood flow and neuronal activity is widely recognized, with blood flow frequently serving as a surrogate for neuronal activity in fMRI studies. At the microscopic level, neuronal activity has been shown to influence blood flow in nearby blood vessels. This study introduces the first predictive model that addresses this issue directly at the explicit neuronal population level. Using in vivo recordings in awake mice, we employ a novel spatiotemporal bimodal transformer architecture to infer current blood flow based on both historical blood flow and ongoing spontaneous neuronal activity. Our findings indicate that incorporating neuronal activity significantly enhances the model's ability to predict blood flow values. Through analysis of the model's behavior, we propose hypotheses regarding the largely unexplored nature of the hemodynamic response to neuronal activity.