Miracle-dz/UniFAR-FTUs huggingface dataset:
Neuroprotective Effect of Moxibustion on Cerebral Ischemia/Reperfusion Injury in Rats by Downregulating NR2B Expression

Objective Stroke is a common and frequently occurring disease of the central nervous system, which is characterized by high mortality and a high disability rate. Moxibustion is a common method for treating stroke in traditional Chinese medicine, but its neuroprotective mechanism is unknown. N-Methyl-D-Aspartate Receptor Subunit 2B (NR2B) plays an important role in neuronal apoptosis. The objective of this study was to explore the mechanisms underlying the neuroprotective effect of moxibustion on cerebral ischemia/reperfusion (I/R) injury based on NR2B. Methods Sprague–Dawley rats were randomly divided into 5 groups: the control group, I/R group, I/R + moxibustion group, I/R + Ro25-6981 (NR2B antagonist) group, and I/R + Ro25-6981 + moxibustion group. The cerebral ischemia/reperfusion model was induced by middle cerebral artery occlusion. Before the establishment of the model, the Ro25-6981 group received intraperitoneal injections of Ro25-6981, the moxibustion group received moxibustion, and the Ro25-6981 + moxibustion group received both interventions. The neurological dysfunction was evaluated by a neurological deficiency score (NDS). The infarct volume was examined by TTC (2,3,5-triphenyltetrazolium chloride) staining. The apoptosis rate of cerebral cells in the ischemic area was examined by TUNEL (terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling) staining, and the expression of Bcl-2, Bax, and caspase-3 was observed by western blot. NR2B and JNK were also observed by western blot. Results Compared with the I/R group, moxibustion significantly decreased the neurological deficiency score (P < 0.05) and the infarct rate (P < 0.01) in I/R rats which were similar to those in the Ro25-6981 group. After moxibustion treatment, there was a significant decrease in the apoptosis rate (P < 0.001) and the protein expression levels of Bax, caspase-3, and JNK (P < 0.001) and an increase in the expression of Bcl-2 (P < 0.01). Compared with the I/R group, moxibustion downregulated the expression of NR2B and decreased the activity of NR2B in the cerebral ischemia area (P < 0.001). Conclusions Moxibustion can improve neurological dysfunction and decrease infarction area and neuronal apoptosis caused by cerebral ischemia/reperfusion in rats. Its neuroprotective mechanism may be related to downregulating the expression of NR2B.
Lignin-containing coatings for packaging materials

Abstract The mechanical properties and chemical stability in water of self-supporting films made from aqueous solutions of starch and lignin, and the barrier properties of paperboard coated with solutions of these polymers have been studied. The dissolution of starch from the starch-lignin films in contact with the model liquids was decreased significantly when lignin was added to the starch films. The addition of ammonium zirconium carbonate (AZC) to the formulations as a crosslinking agent substantially increased the storage modulus of the starch-lignin films, which indicated that crosslinking had occurred. The addition of AZC to the formulations also led to a decrease in dissolution of both starch and lignin from the starch-lignin films in contact with model liquids. The effect of AZC on the water stability of the films was greater when the pH of the starch-lignin-AZC solution was adjusted with ammonia rather than NaOH. The addition of NH4Cl solution as a presumed catalyst to the recipe when the pH adjustment was performed with NaOH did not improve the effect of AZC on the water stability of the films. The water vapour transmission rate of the coated paperboard decreased slightly when AZC was added to the coating formulation.
Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence

The Developmental Origins of Health and Disease (DOHaD) framework aims to understand how early life exposures shape lifecycle health. To date, no comprehensive list of these exposures and their interactions has been developed, which limits our ability to predict trajectories of risk and resiliency in humans. To address this gap, we developed a model that uses text-mining, machine learning, and natural language processing approaches to automate search, data extraction, and content analysis from DOHaD-related research articles available in PubMed. Our first model captured 2469 articles, which were subsequently categorised into topics based on word frequencies within the titles and abstracts. A manual screening validated 848 of these as relevant, which were used to develop a revised model that finally captured 2098 articles that largely fell under the most prominently researched domains related to our specific DOHaD focus. The articles were clustered according to latent topic extraction, and 23 experts in the field independently labelled the perceived topics. Consensus analysis on this labelling yielded mostly from fair to substantial agreement, which demonstrates that automated models can be developed to successfully retrieve and classify research literature, as a first step to gather evidence related to DOHaD risk and resilience factors that influence later life human health.
Kaempferol, Myricetin and Fisetin in Prostate and Bladder Cancer: A Systematic Review of the Literature

Prostate and bladder cancer represent the two most frequently diagnosed genito-urinary malignancies. Diet has been implicated in both prostate and bladder cancer. Given their prolonged latency and high prevalence rates, both prostate and bladder cancer represent attractive candidates for dietary preventive measures, including the use of nutritional supplements. Flavonols, a class of flavonoids, are commonly found in fruit and vegetables and are known for their protective effect against diabetes and cardiovascular diseases. Furthermore, a higher dietary intake of flavonols was associated with a lower risk of both bladder and prostate cancer in epidemiological studies. In this systematic review, we gathered all available evidence supporting the anti-cancer potential of selected flavonols (kaempferol, fisetin and myricetin) against bladder and prostate cancer. A total of 21, 15 and 7 pre-clinical articles on bladder or prostate cancer reporting on kaempferol, fisetin and myricetin, respectively, were found, while more limited evidence was available from animal models and epidemiological studies or clinical trials. In conclusion, the available evidence supports the potential use of these flavonols in prostate and bladder cancer, with a low expected toxicity, thus providing the rationale for clinical trials that explore dosing, settings for clinical use as well as their use in combination with other pharmacological and non-pharmacological interventions.
Boron Doping and Defect Engineering of Graphene Aerogels for Ultrasensitive NO2 Detection

Boron-doped and defect-engineered graphene aerogels are prepared using triphenyl boron as a boron precursor and subsequent heat treatments. The boron chemistry and concentration in the graphene lattice are found to be highly dependent on the temperature used to activate boron. At 1500 °C, boron is incorporated at 3.2 atom % through a combination of B–C, B–N, and B–O bonds. At 1750 °C, the boron concentration decreases to 0.7 atom % and is predominantly incorporated through B–N bonding. Higher temperatures result in complete expulsion of boron from the lattice, leaving behind defects that are found to be beneficial for NO2 gas detection. The gas sensing properties are explored to gain insight into the impact of boron chemistry on the sensing performance. A highly sensitive and selective conductometric NO2 sensor is fabricated on a low-power microheater. Defect-engineered graphene aerogels with no boron remaining have superior gas detection properties. At an optimum sensing temperature of 240 °C, the defect.
Layered Ternary and Quaternary Transition Metal Chalcogenide Based Catalysts for Water Splitting

Water splitting plays an important role in the electrochemical and photoelectrochemical conversion of energy devices. Electrochemical water splitting by the hydrogen evolution reaction (HER) is a straightforward route to producing hydrogen (H2), which requires an efficient electrocatalyst to minimize energy consumption. Recent advances have created a rapid rise in new electrocatalysts, particularly those based on non-precious metals. In this review, we present a comprehensive overview of the recent developments of ternary and quaternary 6d-group transition metal chalcogenides (TMCs) based electrocatalysts for water splitting, especially for HER. Detailed discussion is organized from binary to quaternary TMCs including, surface engineering, heterostructures, chalcogen substitutions and hierarchically structural design in TMCs. Moreover, emphasis is placed on future research scope and important challenges facing these electrocatalysts for further development in their performance towards water splitting.
Patient-and Family-Centered Care 309 to PFCC is critical to addressing racial / ethnic disparities in care and outcomes

As primary care models increasingly include nonphysician team members, more attention should be paid to patientand family-centered care (PFCC) among medical assistants (MAs). The aims of this article are to describe parent perceptions of PFCC by MAs and assess associations between MA PFCC and other perceptions of their care experience. To assess PFCC, we administered the CARE (Consultation and Relational Empathy) measure to the parent/guardian of 360 children following a primary care visit. Perceptions of PFCC by MAs were significantly associated with 6 of 7 other measures of patient experience, including PFCC by the physician, treatment by the receptionist, and satisfaction with wait time. Each team member can potentially shape perceptions of care in ways that could “spill over” into other parts of the visit.
Spatio-Temporal Graph Convolutional and Recurrent Networks for Citywide Passenger Demand Prediction

Online ride-sharing platforms have become a critical part of the urban transportation system. Accurately recommending hotspots to drivers in such platforms is essential to help drivers find passengers and improve users' experience, which calls for efficient passenger demand prediction strategy. However, predicting multi-step passenger demand is challenging due to its high dynamicity, complex dependencies along spatial and temporal dimensions, and sensitivity to external factors (meteorological data and time meta). We propose an end-to-end deep learning framework to address the above problems. Our model comprises three components in pipeline: 1) a cascade graph convolutional recurrent neural network to accurately extract the spatial-temporal correlations within citywide historical passenger demand data; 2) two multi-layer LSTM networks to represent the external meteorological data and time meta, respectively; 3) an encoder-decoder module to fuse the above two parts and decode the representation to predict over multi-steps into the future. The experimental results on three real-world datasets demonstrate that our model can achieve accurate prediction and outperform the most discriminative state-of-the-art methods.
A calibration/validation protocol for long/multi‐pathlength capillary waveguide spectrometers

Long/multi‐pathlength spectrometers are being employed increasingly in ocean science, but detailed calibration/validation protocols for these instruments remain unavailable. The lack of such protocols has led to the collection of absorption data from different instruments that are not always comparable. The goal of this work was to develop procedures that improve inter‐laboratory data reproducibility and accuracy. A World Precision Instrument (WPI) multi‐pathlength liquid capillary waveguide spectrometer along with a Shimadzu dual‐beam spectrometer were employed to evaluate the proposed protocols, which address the following issues: (1) sample cross‐contamination due to improper cell cleaning, (2) nonstandardized sample preparation, (3) errors in effective pathlength determination, and (4) offsets due to refractive index effects (i.e., high‐salinity samples). Effective pathlength calculation is critical given that the measured and manufacturer provided pathlengths can differ significantly, with the provided pathlengths resulting in data with large variation (up to 21%) and percent error (up to 33%) when employing different pathlengths. When these proposed protocols were applied, we found that the WPI spectrometer's precision (measured as a coefficient of variation) was < 0.6% for intra‐day measurements and < 1.5% for inter‐day measurements, and there was < 2% error in absorbance measurements. These procedures allowed for the correction of the significant baseline offsets caused by differences in refractive index between reference and sample, by either subtracting a collected or a generated reference spectrum that matches the offset. Our results indicate that employing the procedures described herein will provide accurate, reproducible, and pathlength‐independent absorption data.
Resource Request Dispatch in Standalone and Federated MEC Systems: A Matching Game Approach

Multi-access edge computing (MEC) system consisting of geographically-distributed heterogeneous servers can provide low-latency virtualized resource to support computation offloading of smart devices. When bulk offloading requests comes to an MEC system, how to dispatch requests to servers so as to maximize divergent objectives of MEC service providers and users is challenging. The problem further involves money transfer when different MEC service providers can share resource to each other. In this paper, we address request dispatch issues in a standalone MEC and among federated MEC systems using matching game theory. We have adapted several classical matching algorithms to our problem. Simulation results show that we can serve more requests while still meeting latency constraints. For federated MEC systems, we can also have high revenue.
ViT-P: Classification of Genitourinary Syndrome of Menopause From OCT Images Based on Vision Transformer Models

Genitourinary syndrome of menopause (GSM) is a disease caused by a physiological decline in estrogen levels, and it can negatively affect a woman’s overall health and quality of life in terms of sexual function. Real-time optical biopsy images can now be obtained with optical coherence tomography (OCT) systems. In this study, we introduce vision transformer (ViT) to the field of medical OCT images for the first time and propose a deep learning-based approach for GSM lesion screening. Specifically, we first build a GSM dataset to train and evaluate the experimental model performance. The study aims to propose a method that combines null convolution with a deep convolutional adversarial generative network classifier to generate the samples needed for training to alleviate the hindrance of such problems, in response to certain practical problems, such as category imbalance that occur during data collection. Next, the experiments present ViT PLUS (ViT-P) for the vaginal OCT image classification task used, which effectively improves the shortcomings of ViT in extracting Patch Embedding using a multibranch convolutional neural network combined with a channel attention mechanism. The clinical images acquired by the OCT device are then automatically classified on the basis of the OCT device to reduce the medical workload of gynecologists. Experimental results show that the ViT-P model outperforms the CNN model and ViT for case screening in the GSM and UCSD datasets, and the accuracy can reach 99.9% and 99.69%, respectively.
A 0.045- to 2.5- GHz Frequency Synthesizer with TDC-based AFC and Phase Switching Multi-Modulus Divider

A 0.045- to 2.5- GHz wideband frequency synthesizer (FS) employing time-to-digital converter (TDC) based automatic frequency calibration (AFC) method and phase switching multi-modulus divider (MMD) for quantization noise suppression is presented in this paper. The counter-based AFC method takes several reference cycles to calculate the instantaneous voltage controlled oscillator (VCO) frequency, while the proposed TDC-based technique needs only 2 cycles. In order to suppress the quantization noise caused by the sigma-delta modulator (SDM) in the MMD, the loop division step is reduced from 2 to 0.5 by adopting the phase switching (PS) technique. The FS is designed and simulated using TSMC 180nm RF CMOS process. The simulation results of the AFC time are less than 1.4 μs when employing a 48 MHz reference signal and the quantization noise is suppressed by 12 dB compared to the conventional MMD structure.
Document-level Neural Machine Translation with Inter-Sentence Attention

Standard neural machine translation (NMT) is on the assumption of document-level context independent. Most existing document-level NMT methods only focus on briefly introducing document-level information but fail to concern about selecting the most related part inside document context. The capacity of memory network for detecting the most relevant part of the current sentence from the memory provides a natural solution for the requirement of modeling document-level context by document-level NMT. In this work, we propose a Transformer NMT system with associated memory network (AMN) to both capture the document-level context and select the most salient part related to the concerned translation from the memory. Experiments on several tasks show that the proposed method significantly improves the NMT performance over strong Transformer baselines and other related studies.
Halloysite nanotubes: Prospects in electrorheology

Electrorheological fluids based on polydimethylsiloxane filled with halloysite nanotubes were studied. The filler structure was characterized by TEM, SEM, and X-ray diffraction. When an electric field is applied to suspensions, their rheological behavior changes – the contribution of the elastic component becomes significant and samples behave like a solid body. The effect of the electric field and filler concentration on the electrorheological behavior was investigated. The influence of water content on the filler structure, as well as on electrorheological and electrophysical properties of suspensions, was considered. Electrorheological fluids filled by halloysite with small water content exhibit slightly higher rheological characteristics under an electric field than dried ones. This study shows the prospects of using halloysite nanotubes as a dispersed phase for electrorheological fluids.
Urban Nature and Biodiversity for Cities

to or lose from the different policies or development scenarios it pursue. to Earth’s climate. Carbon and Sequestration model uses spatial land use and integrates four different carbon pools (above-ground biomass, below-ground biomass, soil, and dead organic matter) to estimate the total amount of carbon stored in a landscape or sequestered over time. Additional data on the market or social value of sequestered carbon, its annual rate of change, and a discount rate can be used to estimate the monetary value of this ecosystem service to society. This model can be used anywhere along the urban-to-rural gradient, from the most densely populated urban core to undeveloped hinterlands.
Stiff Self-Healing Coating Based on UV-Curable Polyurethane with a “Hard Core, Flexible Arm” Structure

Two series of ultraviolet (UV)-cured self-healing polyurethane (PU) oligomers were synthesized through a prepolymer process from isophorone diisocyanate (IPDI) or 1,6-hexamethylene diisocyanate (HDI), polycarbonate diol (PCDL) of varying molecular weight (500, 1000, and 2000 Da), and chemically modified cyclotriphosphazene as hard cores were introduced. The synthesized oligomers contained rigid aromatic rings as “hard cores” and long fatty chains as “flexible arms”. Nuclear magnetic resonance spectrometer (H NMR) and Fourier transform infrared spectroscopy were used to characterize the structures of the oligomers. In addition, the UV-cured self-healing PU coatings were prepared by designing some coating formulations with the PU oligomers. The self-healing properties and mechanical properties of the UV-cured coatings were investigated. The results revealed that the coatings had self-healing properties based on hydrogen bonds. As the molecular weight of PCDL decreased, the coatings exhibited increased hardness, tensile strength, and glass transition temperature. Furthermore, the coatings exhibited excellent thermostability. The results proved the application prospects of the self-healing coatings with high repair efficiency and excellent mechanical properties.
Designing heterogeneous catalysts

This feature issue concerns a collection of six articles all concerned with the topic of designing heterogeneous catalysts. Heterogeneous catalysis is the mainstay of the chemical industry as it is used for producing liquid fuels and chemical intermediates. As such they are essential for the manufacture of the greater majority of manufactured goods worldwide from pharmaceuticals to fertilizers. Indeed, it is considered that about half of the world's population is maintained by a single catalysed reaction, namely the fixation of nitrogen from the air to make ammonia, and this involves an iron catalyst. In this way, catalysis, in general, contributes about 20–25% of global GDP—it is therefore a topic of immense significance. Although we have …
Uma Estratégia para Gerência do Processo baseada nos Custos da Qualidade de Software

Este trabalho apresenta uma estratégia para gerência do processo de produção de software, voltada à proposição de ações técnicas e gerenciais que conduzam a melhorias do seu processo de produção. Utiliza-se um enfoque baseado em aspectos eminentemente quantitativos, que permitem integrar a gerência das características técnicas dos produtos desenvolvidos, representada pelo seu controle de configuração, com as características administrativas ligadas à gerência de sua produção, enfocadas pelos controles de custos de desenvolvimento e dos custos da qualidade.
High-Accuracy CSI Feedback With Super-Resolution Network for Massive MIMO Systems

Acquiring accurate channel state information (CSI) is critical for downlink precoding in frequency division duplexity (FDD) massive multiple-input multiple-output (MIMO) systems. In contrast to the traditional compressive sensing (CS) based methods, whose performance is hindered by excessive feedback overhead, this letter proposes a super-resolution network (SRNet) to compress and reconstruct the CSI. Specifically, the SRNet consists of encoder and decoder, where the encoder can transform channel matrices into codewords, and the decoder can restore different levels of spatial frequency features of CSI image based on a modified embedded block residual network (EBRN+). In addition, a principal component mark (PCM) method is proposed before encoding to lighten the encoder at UE. The experiment results show that our proposed model can achieve better performance than the state-of-the-art models with less training parameters and lower computational complexity at UE. Moreover, the superiority of our proposed model becomes much more significant especially under high compression ratio scenarios.