Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping Temperature Distribution Generated by Photothermal Conversion in Graphene Film Using Er,Yb:NaYF4 Nanoparticles Prepared by Microwave-Assisted Solvothermal Method

This study analyzes the mapping of temperature distribution generated by graphene in a glass slide cover after illumination at 808 nm with a good thermal resolution. For this purpose, Er,Yb:NaYF4 nanoparticles prepared by a microwave-assisted solvothermal method were used as upconversion luminescent nanothermometers. By tuning the basic parameters of the synthesis procedure, such as the time and temperature of reaction and the concentration of ethanol and water, we were able to control the size and the crystalline phase of the nanoparticles, and to have the right conditions to obtain 100% of the β hexagonal phase, the most efficient spectroscopically. We observed that the thermal sensitivity that can be achieved with these particles is a function of the size of the nanoparticles and the crystalline phase in which they crystallize. We believe that, with suitable changes, these nanoparticles might be used in the future to map temperature gradients in living cells while maintaining a good thermal resolution.
Rifaximin Alters Intestinal Microbiota and Prevents Progression of Ankylosing Spondylitis in Mice

Recently, accumulating evidence has suggested that gut microbiota may be involved in the occurrence and development of ankylosing spondylitis (AS). It has been suggested that rifaximin have the ability to modulate the gut bacterial communities, prevent inflammatory response, and modulate gut barrier function. The goal of this work is to evaluate the protective effects of rifaximin in fighting AS and to elucidate the potential underlying mechanism. Rifaximin were administered to the proteoglycan (PG)-induced AS mice for 4 consecutive weeks. The disease severity was measured with the clinical and histological of arthritis and spondylitis. Intestinal histopathological, pro-inflammatory cytokine levels and the intestinal mucosal barrier were evaluated. Then, western blot was performed to explore the toll-like receptor 4 (TLR-4) signal transducer and NF-κB expression. Stool samples were collected to analyze the differences in the gut microbiota via next-generation sequencing of 16S rDNA. We found that rifaximin significantly reduced the severity of AS and resulted in down-regulation of inflammatory factors, such as TNF-α, IL-6, IL-17A, and IL-23. Meanwhile, rifaximin prevented ileum histological alterations, restored intestinal barrier function and inhibited TLR-4/NF-κB signaling pathway activation. Rifaximin also changed the gut microbiota composition with increased Bacteroidetes/Firmicutes phylum ratio, as well as selectively promoting some probiotic populations, including Lactobacillales. Our results suggest that rifaximin suppressed progression of AS and regulated gut microbiota in AS mice. Rifaximin might be useful as a novel treatment for AS.
Development of Electrochemical Nanosensor for the Detection of Malaria Parasite in Clinical Samples

In this study, electrochemical nanosensors were developed from the synthesized metal oxide (MO) nanoparticles by supporting it on a gold electrode (Au). The activity of the developed nanosensor toward the detection of malaria biomarker (β-hematin) was determined and the optimum conditions at which the maximum detection and quantification occurred were established. β-Hematin current response at the sensors was higher when compared with the bare Au electrode and followed the order Au-CuO (C) > Au-CuO (M) > Au-Fe2O3 (M) > Au-Fe2O3 (C) > Au-Al2O3 (M) > Au-Al2O3 (C) > bare Au. The developed sensors were stable with a relatively low current drop (10.61–17.35 %) in the analyte. Au-CuO sensor had the best performance toward the biomarker and quantitatively detected P. berghei in infected mice's serum samples at 3.60–4.8 mM and P. falciparum in human blood serum samples at 0.65–1.35 mM concentration.
Sodium chloride is an ionic checkpoint for human TH2 cells and shapes the atopic skin microenvironment

Sodium chloride induces and amplifies human TH2 cell responses, potentially shaping the atopic skin microenvironment. Salting T cells’ game Sodium chloride can encourage TH17 differentiation in CD4 T cells. Matthias et al. now show that sodium chloride can also promote TH2 responses, which are central in allergic diseases. They measured effects of different concentrations of sodium chloride on memory and naïve human T cells as well as mouse cells. Collectively, higher sodium chloride enforced a TH2 transcriptional and phenotypic program. Interestingly, lesional skin from atopic dermatitis patients, but not psoriasis patients, had elevated sodium. These findings suggest that sodium chloride could be a potential player in the progression of allergy in the skin. The incidence of allergic diseases has increased over the past 50 years, likely due to environmental factors. However, the nature of these factors and the mode of action by which they induce the type 2 immune deviation characteristic of atopic diseases remain unclear. It has previously been reported that dietary sodium chloride promotes the polarization of T helper 17 (TH17) cells with implications for autoimmune diseases such as multiple sclerosis. Here, we demonstrate that sodium chloride also potently promotes TH2 cell responses on multiple regulatory levels. Sodium chloride enhanced interleukin-4 (IL-4) and IL-13 production while suppressing interferon-γ (IFN-γ) production in memory T cells. It diverted alternative T cell fates into the TH2 cell phenotype and also induced de novo TH2 cell polarization from naïve T cell precursors. Mechanistically, sodium chloride exerted its effects via the osmosensitive transcription factor NFAT5 and the kinase SGK-1, which regulated TH2 signature cytokines and master transcription factors in hyperosmolar salt conditions. The skin of patients suffering from atopic dermatitis contained elevated sodium compared to nonlesional atopic and healthy skin. These results suggest that sodium chloride represents a so far overlooked cutaneous microenvironmental checkpoint in atopic dermatitis that can induce TH2 cell responses, the orchestrators of atopic diseases.
The nonlesional skin surface distinguishes atopic dermatitis with food allergy as a unique endotype

Patients with atopic dermatitis and food allergy have an immature epithelial barrier and type 2 immune activation in their skin. Digging skin deep Disrupted epithelial barriers are thought to be central to development of allergic disorders such as atopic dermatitis, which is commonly associated with food allergy. Leung et al. performed repeated skin tape stripping on lesional and nonlesional skin of patients with pediatric atopic dermatitis, some of whom also had food allergies. Among other parameters, they measured lipids, proteins, barrier integrity, and the microbiome. They found that nonlesional skin from patients with atopic dermatitis had different characteristics depending on whether the patient also had food allergy. Gene expression analysis also showed elevated type 2 immune signatures in food allergic samples. Their findings reveal unique properties about the epidermis in allergic disease and could lead to the development of biomarkers for food allergy. Skin barrier dysfunction has been reported in both atopic dermatitis (AD) and food allergy (FA). However, only one-third of patients with AD have FA. The purpose of this study was to use a minimally invasive skin tape strip sampling method and a multiomics approach to determine whether children with AD and FA (AD FA+) have stratum corneum (SC) abnormalities that distinguish them from AD without FA (AD FA−) and nonatopic (NA) controls. Transepidermal water loss was found to be increased in AD FA+. Filaggrin and the proportion of ω-hydroxy fatty acid sphingosine ceramide content in nonlesional skin of children with AD FA+ were substantially lower than in AD FA− and NA skin. These abnormalities correlated with morphologic changes in epidermal lamellar bilayer architecture responsible for barrier homeostasis. Shotgun metagenomic studies revealed that the nonlesional skin of AD FA+ had increased abundance of Staphylococcus aureus compared to NA. Increased expression of keratins 5, 14, and 16 indicative of hyperproliferative keratinocytes was observed in the SC of AD FA+. The skin transcriptome of AD FA+ had increased gene expression for dendritic cells and type 2 immune pathways. A network analysis revealed keratins 5, 14, and 16 were positively correlated with AD FA+, whereas filaggrin breakdown products were negatively correlated with AD FA+. These data suggest that the most superficial compartment of nonlesional skin in AD FA+ has unique properties associated with an immature skin barrier and type 2 immune activation.
Estimating cholera incidence with cross-sectional serology

Cross-sectional Vibrio cholerae–related antibody measures can be used to estimate cholera incidence in a population. Estimating the true prevalence of cholera Successful development of anti-cholera measures requires accurate estimates of infection incidence. Reporting of cholera cases, however, typically relies on clinical assessment at the time of patient presentation and can be problematized by lack of access to health care and variable, nonspecific symptomatology. Combining a small number of serological markers with machine learning methods, Azman et al. were able to accurately detect individuals who had had cholera infections within the previous year. Simulated serosurveys showed that this simple antibody-based approach could potentially be used as an alternative method to estimate cholera incidence in a population. The development of new approaches to cholera control relies on an accurate understanding of cholera epidemiology. However, most information on cholera incidence lacks laboratory confirmation and instead relies on surveillance systems reporting medically attended acute watery diarrhea. If recent infections could be identified using serological markers, cross-sectional serosurveys would offer an alternative approach to measuring incidence. Here, we used 1569 serologic samples from a cohort of cholera cases and their uninfected contacts in Bangladesh to train machine learning models to identify recent Vibrio cholerae O1 infections. We found that an individual’s antibody profile contains information on the timing of V. cholerae O1 infections in the previous year. Our models using six serological markers accurately identified individuals in the Bangladesh cohort infected within the last year [cross-validated area under the curve (AUC), 93.4%; 95% confidence interval (CI), 92.1 to 94.7%], with a marginal performance decrease using models based on two markers (cross-validated AUC, 91.0%; 95% CI, 89.2 to 92.7%). We validated the performance of the two-marker model on data from a cohort of North American volunteers challenged with V. cholerae O1 (AUC range, 88.4 to 98.4%). In simulated serosurveys, our models accurately estimated annual incidence in both endemic and epidemic settings, even with sample sizes as small as 500 and annual incidence as low as two infections per 1000 individuals. Cross-sectional serosurveys may be a viable approach to estimating cholera incidence.
Precipitation strengthening of aluminum alloys by room-temperature cyclic plasticity

Pushing and pulling for high strength High-strength aluminum alloys are important for producing lightweight cars, trains, and airplanes. The traditional strategy for doing this is through hours of high-temperature cycling to form precipitates in the alloy. Sun et al. developed a processing method that relies on mechanical cycling by pushing and pulling on the alloys at room temperature. This quickly creates many very fine precipitates that have the same strengthening effect as those characteristic of traditional thermal methods. This method should also work for other alloy systems. Science, this issue p. 972 An alloy-processing method using mechanical cycling produces high-strength aluminum alloys. High-strength aluminum alloys are important for lightweighting vehicles and are extensively used in aircraft and, increasingly, in automobiles. The highest-strength aluminum alloys require a series of high-temperature “bakes” (120° to 200°C) to form a high number density of nanoparticles by solid-state precipitation. We found that a controlled, room-temperature cyclic deformation is sufficient to continuously inject vacancies into the material and to mediate the dynamic precipitation of a very fine (1- to 2-nanometer) distribution of solute clusters. This results in better material strength and elongation properties relative to traditional thermal treatments, despite a much shorter processing time. The microstructures formed are much more uniform than those characteristic of traditional thermal treatments and do not exhibit precipitate-free zones. These alloys are therefore likely to be more resistant to damage.
Specificity, versatility, and control of TGF-β family signaling

Molecular function and pathway cross-talk in TGF-β signaling reveal its complexity and open questions. Gloss The transforming growth factor–β (TGF-β) family of secreted proteins is implicated in the control of widely diverse physiologies and its dysregulation with disease. In this review, which contains 7 figures and 343 references, we describe the exquisite nature of TGF-β family signaling in its roles in diverse and context-specific cellular behaviors. These pathways are far from linear and still far from being fully illuminated. Encoded in mammalian cells by 33 genes, the transforming growth factor–β (TGF-β) family of secreted, homodimeric and heterodimeric proteins controls the differentiation of most, if not all, cell lineages and many aspects of cell and tissue physiology in multicellular eukaryotes. Deregulation of TGF-β family signaling leads to developmental anomalies and disease, whereas enhanced TGF-β signaling contributes to cancer and fibrosis. Here, we review the fundamentals of the signaling mechanisms that are initiated upon TGF-β ligand binding to its cell surface receptors and the dependence of the signaling responses on input from and cooperation with other signaling pathways. We discuss how cells exquisitely control the functional presentation and activation of heteromeric receptor complexes of transmembrane, dual-specificity kinases and, thus, define their context-dependent responsiveness to ligands. We also introduce the mechanisms through which proteins called Smads act as intracellular effectors of ligand-induced gene expression responses and show that the specificity and impressive versatility of Smad signaling depend on cross-talk from other pathways. Last, we discuss how non-Smad signaling mechanisms, initiated by distinct ligand-activated receptor complexes, complement Smad signaling and thus contribute to cellular responses.
Brain metabolism modulates neuronal excitability in a mouse model of pyruvate dehydrogenase deficiency

Decreased metabolic flux due to deficient mitochondrial pyruvate dehydrogenase causes excitability dysfunction that can be modulated by acetate. An alternative energy source for PDHD Pyruvate dehydrogenase deficiency (PDHD) is a rare disorder caused by altered glucose metabolism. The symptoms of PDHD are predominantly neurological, and patients develop intractable epilepsy early in infancy. However, the mechanisms responsible for the increased brain excitability are mostly unknown. To address this issue, Jakkamsetti et al. developed a mouse model of PDHD recapitulating the neurological symptoms of the human disease. The authors found that seizure activity was mediated by reduction of the mitochondrial tricarboxylic acid (TCA) cycle flux and decreased excitability in a population of putative inhibitory neurons. Administration of acetate, an alternative metabolic substrate, restored TCA activity and reduced seizure duration. Glucose is the ultimate substrate for most brain activities that use carbon, including synthesis of the neurotransmitters glutamate and γ-aminobutyric acid via mitochondrial tricarboxylic acid (TCA) cycle. Brain metabolism and neuronal excitability are thus interdependent. However, the principles that govern their relationship are not always intuitive because heritable defects of brain glucose metabolism are associated with the paradoxical coexistence, in the same individual, of episodic neuronal hyperexcitation (seizures) with reduced basal cerebral electrical activity. One such prototypic disorder is pyruvate dehydrogenase (PDH) deficiency (PDHD). PDH is central to metabolism because it steers most of the glucose-derived flux into the TCA cycle. To better understand the pathophysiology of PDHD, we generated mice with brain-specific reduced PDH activity that paralleled salient human disease features, including cerebral hypotrophy, decreased amplitude electroencephalogram (EEG), and epilepsy. The mice exhibited reductions in cerebral TCA cycle flux, glutamate content, spontaneous, and electrically evoked in vivo cortical field potentials and gamma EEG oscillation amplitude. Episodic decreases in gamma oscillations preceded most epileptiform discharges, facilitating their prediction. Fast-spiking neuron excitability was decreased in brain slices, contributing to in vivo action potential burst prolongation after whisker pad stimulation. These features were partially reversed after systemic administration of acetate, which augmented cerebral TCA cycle flux, glutamate-dependent synaptic transmission, inhibition and gamma oscillations, and reduced epileptiform discharge duration. Thus, our results suggest that dysfunctional excitability in PDHD is consequent to reduced oxidative flux, which leads to decreased neuronal activation and impaired inhibition, and can be mitigated by an alternative metabolic substrate.
Multi-Tone Signal Optimization for Wireless Power Transfer in the Presence of Wireless Communication Links

In this paper, we study optimization of multi-tone signals for wireless power transfer (WPT) systems. We investigate different non-linear energy harvesting models. Two of them are adopted to optimize the multi-tone signal according to the channel state information available at the transmitter. We show that a second-order polynomial curve-fitting model can be utilized to optimize the multi-tone signal for any RF energy harvester design. We consider both single-antenna and multi-antenna WPT systems. In-band co-existing communication links are also considered in this work by imposing a constraint on the received power at the nearby information receiver to prevent its RF front end from saturation. We emphasize the importance of imposing such constraint by explaining how inter-modulation products, due to saturation, can cause high interference at the information receiver in the case of multi-tone signals. The multi-tone optimization problem is formulated as a non-convex linearly constrained quadratic program. Two globally optimal solution approaches using mixed-integer linear programming and finite branch-and-bound techniques are proposed to solve the problem. The achieved improvement resulting from applying both solution methods to the multi-tone optimization problem is highlighted through simulations and comparisons with other solutions existing in the literature.
Visualisation of Pareto Front Approximation: A Short Survey and Empirical Comparisons

Visualisation is an effective way to facilitate the analysis and understanding of multivariate data. In the context of multi-objective optimisation, comparing to quantitative performance metrics, visualisation is, in principle, able to provide a decision maker better insights about Pareto front approximation sets (e.g. the distribution of solutions, the geometric characteristics of Pareto front approximation) thus to facilitate the decision-making (e.g. the exploration of trade-off relationship, the knee region or region of interest). In this paper, we overview some currently prevalent visualisation techniques according to the way how data is represented. To have a better understanding of the pros and cons of different visualisation techniques, we empirically compare six representative visualisation techniques for the exploratory analysis of different Pareto front approximation sets obtained by four state-of-the-art evolutionary multi-objective optimisation algorithms on the classic DTLZ benchmark test problems. From the empirical results, we find that visual comparisons also follow the No-Free-Lunch theorem where no single visualisation technique is able to provide a comprehensive understanding of the characteristics of a Pareto front approximation set. In other words, a specific type of visualisation technique is only good at exploring a particular aspect of the data.
Selective Sensor Fusion for Neural Visual-Inertial Odometry

Deep learning approaches for Visual-Inertial Odometry (VIO) have proven successful, but they rarely focus on incorporating robust fusion strategies for dealing with imperfect input sensory data. We propose a novel end-to-end selective sensor fusion framework for monocular VIO, which fuses monocular images and inertial measurements in order to estimate the trajectory whilst improving robustness to real-life issues, such as missing and corrupted data or bad sensor synchronization. In particular, we propose two fusion modalities based on different masking strategies: deterministic soft fusion and stochastic hard fusion, and we compare with previously proposed direct fusion baselines. During testing, the network is able to selectively process the features of the available sensor modalities and produce a trajectory at scale. We present a thorough investigation on the performances on three public autonomous driving, Micro Aerial Vehicle (MAV) and hand-held VIO datasets. The results demonstrate the effectiveness of the fusion strategies, which offer better performances compared to direct fusion, particularly in presence of corrupted data. In addition, we study the interpretability of the fusion networks by visualising the masking layers in different scenarios and with varying data corruption, revealing interesting correlations between the fusion networks and imperfect sensory input data.
Noncoherent and Non-orthogonal Massive SIMO for Critical Industrial IoT Communications

Towards the realization of ultra-reliable low-latency wireless communications required in critical industrial Internet of Things (IIoT) applications, this paper presents a new noncoherent and non-orthogonal massive single-input multiple-output (SIMO) framework, in which a large number of antennas are deployed to provide high spatial diversity so as to enable ultra-reliable communications, and noncoherent transmission and non-orthogonal multiple access techniques are applied to effectively reduce the latency at the physical and data link layers, respectively. A two-user IIoT system is considered to elaborate the key design principle of our framework, in which two controlled nodes (CNs) transmit their data to a common managing node (MN) on the same time-frequency resource block, and the MN implements the noncoherent maximum likelihood detector to recover the transmitted symbols of both CNs from the received sum signal. We analyze the error performance of the considered system and then minimize the system error probability by jointly designing the constellations of both CNs. Simulation results show that our design has lower error probability than existing designs.