Miracle-dz/UniFAR-FTUs huggingface dataset:
Preclinical Characterization of Antioxidant Quinolyl Nitrone QN23 as a New Candidate for the Treatment of Ischemic Stroke

Nitrones are encouraging drug candidates for the treatment of oxidative stress-driven diseases such as acute ischemic stroke (AIS). In a previous study, we found a promising quinolylnitrone, QN23, which exerted a neuroprotective effect in neuronal cell cultures subjected to oxygen–glucose deprivation and in experimental models of cerebral ischemia. In this paper, we update the biological and pharmacological characterization of QN23. We describe the suitability of intravenous administration of QN23 to induce neuroprotection in transitory four-vessel occlusion (4VO) and middle cerebral artery occlusion (tMCAO) experimental models of brain ischemia by assessing neuronal death, apoptosis induction, and infarct area, as well as neurofunctional outcomes. QN23 significantly decreased the neuronal death and apoptosis induced by the ischemic episode in a dose-dependent manner and showed a therapeutic effect when administered up to 3 h after post-ischemic reperfusion onset, effects that remained 11 weeks after the ischemic episode. In addition, QN23 significantly reduced infarct volume, thus recovering the motor function in a tMCAO model. Remarkably, we assessed the antioxidant activity of QN23 in vivo using dihydroethidium as a molecular probe for radical species. Finally, we describe QN23 pharmacokinetic parameters. All these results pointing to QN23 as an interesting and promising preclinical candidate for the treatment of AIS.
Characterizing Heterogeneous Traffic Flow at a Slope Bottleneck via Cellular Automaton Model

This paper considers the unique attributes of autonomous vehicles, the characteristics of expressways’ minor actual slope grade, and the speed update integer requirements of discrete cellular automata (CA). The continuous deceleration efficiency of the uphill is discretized by introducing variable parameters connected to the vehicle position and gradient, and the hybrid traffic flow CA model suitable for slope bottlenecks based on the principle of the safety distance is further improved. The simulation results show that the improved model can simulate the unique traffic flow phenomenon caused by the change in slope conditions. There is a critical mutation in the traffic flow phase on the slope road section. Secondly, according to the different main factors influencing traffic flow, free flow, terrain jams, and homogeneous congested flow coexist on the slope section. The traffic flow in the crowded terrain area is significantly negatively correlated with the length and longitudinal gradient of the slope, and increasing the longitudinal angle of the hill will extend the congested terrain area. In addition, the influence of the change in slope conditions on mixed traffic flow has a critical longitudinal gradient of 3% and a fixed critical slope length. In non-free flow areas, autonomous vehicles can significantly alleviate traffic congestion on upslopes.
A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery

Accurate estimation of lithium-ion battery state-of-health (SOH) is important for the safe operation of electric vehicles; however, in practical applications, the accuracy of SOH estimation is affected by uncertainty factors, including human operation, working conditions, etc. To accurately estimate the battery SOH, a hybrid neural network based on the dilated convolutional neural network and the bidirectional gated recurrent unit, namely dilated CNN-BiGRU, is proposed in this paper. The proposed data-driven method uses the voltage distribution and capacity changes in the extracted battery discharge curve to learn the serial data time dependence and correlation. This method can obtain more accurate temporal and spatial features of the original battery data, resulting higher accuracy and robustness. The effectiveness of dilated CNN-BiGRU for SOH estimation is verified on two publicly lithium-ion battery datasets, the NASA Battery Aging Dataset and Oxford Battery Degradation Dataset. The experimental results reveal that the proposed model outperforms the compared data-driven methods, e.g., CNN-series and RNN-series. Furthermore, the mean absolute error (MAE) and root mean square error (RMSE) are limited to within 1.9% and 3.3%, respectively, on the NASA Battery Aging Dataset.
Analyzing Trajectories of Acute Cigarette Reduction Post-Introduction of an E-Cigarette Using Ecological Momentary Assessment Data

Electronic cigarettes (ECs) may hold great potential for helping smokers transition off combustible cigarettes (CCs); however, little is known about the patterns that smokers follow when using an EC as a CC-substitute in order to ultimately reduce and quit smoking. Our primary aim in this study was to evaluate whether common patterns of CC use exist amongst individuals asked to substitute an EC for at least half of the CCs they would normally smoke. These patterns may elucidate the immediate switching and reduction behaviors of individuals using ECs as a reduction/cessation tool. This analysis uses data from a randomized controlled trial of 84 adult smokers assigned to receive either 4.5% nicotine or placebo (0% nicotine) EC. Participants were advised to use the EC to help them reach a 50% reduction in cigarettes-per-day (CPD) within 3 weeks. Longitudinal trajectory analysis was used to identify CPD reduction classes amongst the sample; participants clustered into four distinct, linear trajectories based on daily CC use during the 3-week intervention. Higher readiness to quit smoking, prior successful quit attempts, and lower baseline CC consumption were associated with assignment into “more successful” CC reduction classes. ECs may be a useful mechanism to promote CC reduction. This study demonstrates that a fine-grained trajectory approach can be applied to examine switching patterns in the critical first weeks of an attempt.
How preschoolers perceive danger – A study of inattentional bias

ABSTRACT Harmful threats can sometimes appear unexpectedly in the lives of young children, whose limited experience leads to a greater risk of endangerment. The current study adapted the Variant Odd Ball protocol to explore the effects of threat and familiarity on inattentional blindness (IB). This research evaluated reactions to presentations of evolutionarily relevant images such as millipedes, snakes, escargots, and snails, compared with reactions to evolutionarily irrelevant images such as syringes, knives, flashlights, and spoons. Respondents included three hundred and forty 4–5-year-old preschool children. The findings were as follows: (1) The property of familiarity plays an important role in preschool children’s IB to both evolutionarily relevant and irrelevant images, and highly familiar images were easier to detect than less familiar images; (2) the manipulation of images showed that the threat-relevant stimuli were not more likely to be detected than the non-threat-relevant stimuli; (3) no significant difference was found between the detection rates of evolutionarily relevant and irrelevant images. Preschool children have a lower detection rate of threat-relevant images (e.g., snake, knife) which may reflect their limited experience of danger and weaker integration of these threat-relevant images in the current study.
Exploiting European GNSS and Ethereum in location proof systems

ABSTRACT Location-Based Services (LBSs) are essential in many application contexts like ride-sharing or navigation apps. There are cases where users could gain an advantage by submitting fake locations. The problem faced in this paper concerns the possibility that the geographic location declared by a user is not the actual location in which the user is placed. Some solutions are based on centralized or distributed verification in the literature, and other solutions are based on witnesses or infrastructure. In this paper, we highlight the limitations of such approaches and propose a new scheme that exploits signals coming from satellites to provide trustworthy location proofs, also respecting users' privacy. The proposed approach is decentralized because location proofs are stored by users in a suitably-encrypted way, and a blockchain is adopted to guarantee data integrity and authenticity. We show that the proposed approach overcomes the state of the art through a detailed analysis.
Gene Therapy for Mitochondrial Diseases: Current Status and Future Perspective

Mitochondrial diseases (MDs) are a group of severe genetic disorders caused by mutations in the nuclear or mitochondrial genome encoding proteins involved in the oxidative phosphorylation (OXPHOS) system. MDs have a wide range of symptoms, ranging from organ-specific to multisystemic dysfunctions, with different clinical outcomes. The lack of natural history information, the limits of currently available preclinical models, and the wide range of phenotypic presentations seen in MD patients have all hampered the development of effective therapies. The growing number of pre-clinical and clinical trials over the last decade has shown that gene therapy is a viable precision medicine option for treating MD. However, several obstacles must be overcome, including vector design, targeted tissue tropism and efficient delivery, transgene expression, and immunotoxicity. This manuscript offers a comprehensive overview of the state of the art of gene therapy in MD, addressing the main challenges, the most feasible solutions, and the future perspectives of the field.
The Gauss-Krueger projection: Karney-Krueger equations

The Gauss-Krueger projection has two forms. One has the Karney-Krueger equations capable of micrometre accuracy anywhere within 30° of a central meridian of longitude. The other has equations limited to millimetre accuracy within 6° of a central meridian. These latter equations are complicated but are widely used. The former equations are simple, easily adapted to computers, but not in wide use. This paper gives a complete development of the Karney-Krueger equations.
Grape-Seed Procyanidin Extract (GSPE) Seasonal-Dependent Modulation of Glucose and Lipid Metabolism in the Liver of Healthy F344 Rats

Seasonality is gaining attention in the modulation of some physiological and metabolic functions in mammals. Furthermore, the consumption of natural compounds, such as GSPE, is steadily increasing. Consequently, in order to study the interaction of seasonal variations in day length over natural compounds’ molecular effects, we carried out an animal study using photo-sensitive rats which were chronically exposed for 9 weeks to three photoperiods (L6, L18, and L12) in order to mimic the day length of different seasons (winter/summer/and autumn-spring). In parallel, animals were also treated either with GSPE 25 (mg/kg) or vehicle (VH) for 4 weeks. Interestingly, a seasonal-dependent GSPE modulation on the hepatic glucose and lipid metabolism was observed. For example, some metabolic genes from the liver (SREBP-1c, Gk, Acacα) changed their expression due to seasonality. Furthermore, the metabolomic results also indicated a seasonal influence on the GSPE effects associated with glucose-6-phosphate, D-glucose, and D-ribose, among others. These differential effects, which were also reflected in some plasmatic parameters (i.e., glucose and triglycerides) and hormones (corticosterone and melatonin), were also associated with significant changes in the expression of several hepatic circadian clock genes (Bmal1, Cry1, and Nr1d1) and ER stress genes (Atf6, Grp78, and Chop). Our results point out the importance of circannual rhythms in regulating metabolic homeostasis and suggest that seasonal variations (long or short photoperiods) affect hepatic metabolism in rats. Furthermore, they suggest that procyanidin consumption could be useful for the modulation of the photoperiod-dependent changes on glucose and lipid metabolism, whose alterations could be related to metabolic diseases (e.g., diabetes, obesity, and cardiovascular disease). Furthermore, even though the GSPE effect is not restricted to a specific photoperiod, our results suggest a more significant effect in the L18 condition.
Epidemiology of blaCTX-M-Positive Salmonella Typhimurium From Diarrhoeal Outpatients in Guangdong, China, 2010–2017

Salmonella enterica can lead to intestinal diarrhea, and the emergence and spread of cephalosporin-resistant Salmonella have brought great challenges to clinical treatment. Therefore, this study investigated the prevalence and transmission of blaCTX-M genes among S. Typhimurium from diarrhoeal outpatients in Guangdong, China, from 2010 to 2017. A total of 221 blaCTX-M-positive isolates were recovered from 1,263 S. Typhimurium isolates from the facal samples of diarrhoea patients in 45 general hospitals from 11 cities. The most popular CTX-M gene was blaCTX-M-55 (39.6%, 72/182) in the CTX-M-1 group, followed by blaCTX-M-14 (22.5%, 41/182) and blaCTX-M-65 (19.2%, 35/182) in the CTX-M-9 group. The isolates that carried blaCTX-M-9G had significantly higher resistance rates to multiple antibacterials compared with blaCTX-M-1G (p < 0.01). Meanwhile, PFGE analysis not only showed the clonal transmission of blaCTX-M-55/14/65-positve isolates of diarrhoeal outpatients’ origins from different hospitals in Guangdong province, but also the characteristic of blaCTX-M-55/14/65-positve isolates’ bacterial persistence. Multilocus sequence typing (MLST) analysis indicated that these S. Typhimurium isolates possessed ST34 and ST19. Furthermore, genomic Beast phylogenomic analysis provided the evidence of a close relationship of blaCTX-M-positive S. Typhimurium isolates between the outpatients and pork. Most blaCTX-M-55/14/65 genes were transmitted by non-typeable or IncI1/IncFII/IncHI2 plasmids with the size of ranging from ~80 to ~280 kb. Moreover, whole-genome sequencing (WGS) analysis further revealed that blaCTX-M-55/14/65 coexisted with other 25 types of ARGs, of which 11 ARGs were highly prevalent with the detection rates >50%, and it first reported the emergence of blaTEM-141 in S. Typhimurium. This study underscores the importance of surveillance for blaCTX-M-positive microbes in diarrhea patients.
Parenting in a new world: migration, social class, and parenting, and links to academic outcomes in China

Abstract Existing research has conceptualized educational inequalities between migrant and urban youth in China primarily as an issue of differential access to education. Focusing on parenting practices and the intersection of migrant and social class status—and in particular parental education—we use a novel dataset of six-year-olds in Shanghai and show that although migrants, on average, use less effective parenting practices than urbanites overall, these differences reverse, or lessen, after taking parental education into account. Moreover, parental education is more important for both parenting and children’s education outcomes for migrant families than urban families, suggesting the differential importance of social class for migrant groups.
Collective Intelligence using 5G: Concepts, Applications, and Challenges in Sociotechnical Environments

Distributed intelligence is a well-known approach for optimizing interactions among numerous smart devices that interconnect and operate together as Internet of Things (IoT) systems. A modern form of human-machine collective intelligence emerges when humans interact with IoT systems in sociotechnical environments such as smart homes. Fifth-generation (5G) communication networks are designed for high-speed reliable wireless connectivity and expected to boost IoT and (distributed) collective intelligence by revolutionizing human–device–human interactions. In this paper, we contribute a comprehensive review of state-of-the-art sociotechnical environments that exhibit collective intelligence, supported by 5G-enabled IoT. We discuss the latest developments in 5G and their implications for collective intelligence. Further, we explain the key challenges for using 5G to support collective intelligence, e.g., data processing, security, and radio resource management. Finally, we describe four practical applications of collective intelligence to sociotechnical environments—road traffic control, unmanned aerial vehicles, electrical load demand response, and augmented democracy.
Morphological and Phylogenetic Analyses Reveal Five New Species in Chaetosphaeriaceae

Chaetosphaeriaceae is a genera-rich and highly diverse group of fungi with a worldwide distribution in terrestrial and aquatic habitats. Eight fresh collections of Chaetosphaeriaceae were obtained during investigations of hyaline-spored hyphomycetes in China and Thailand. Based on morphological characteristics and phylogenetic analysis of a combined LSU and ITS sequence dataset, Chaetosphaeria obovoidea, Codinaea aseptata, Codinaeella hyalina, Dictyochaeta guizhouensis and Paragaeumannomyces guttulatus were introduced as new species, Codinaea terminalis was reported as new host record, and Codinaea dwaya and Phialosporostilbe scutiformis were introduced as new collections. Phylogenetic analysis in this study revealed that Chaetosphaeria was polyphyletic. Detailed descriptions and illustrations of new taxa and identified species are provided, as well as an updated phylogenetic tree to confirm the placements of these eight new collections.
Enterprise Profitability and Financial Evaluation Model Based on Statistical Modeling: Taking Tencent Music as an Example

In today’s diversified development model, the combination of modeling and business decision development is particularly important. The advanced theoretical business model established by modeling enables more efficient and accurate financial analysis. In the original enterprise profit evaluation model, the DuPont analysis method cannot take into account the development capability of the enterprise very well. This article takes Tencent Music as an example, and improves it on the basis of DuPont analysis. The Enterprise Capital Profit Model was proposed. At the same time, the LASSO regression based on cluster analysis is used to screen, analyze, and diagnose the financial data of Tencent Music in recent years, which verifies the validity and feasibility of the model. This paper uses the report data combined with statistical modeling to optimize the traditional financial evaluation method of enterprises, better find problems, and provide strategies for the further development of enterprises. Likewise, the method can be extended to other businesses to help them analyze their financial situation and provide a reference for future development.
Ginsenoside Rh3 Inhibits Lung Cancer Metastasis by Targeting Extracellular Signal-Regulated Kinase: A Network Pharmacology Study

Lung cancer has a high mortality rate and is very common. One of the main reasons for the poor prognosis of patients with lung cancer is the high incidence of metastasis. Ginsenoside Rh3, a rare ginsenoside extracted from Panax notoginseng, exhibits excellent anti-inflammatory and anti-tumor effects. Nonetheless, the inhibitory potential of Rh3 against lung cancer remains unknown. The target genes of Rh3 were screened by the PharmMapper database; the proliferation of lung cancer cells was detected by MTT assay; the migration and invasion of cells were detected by the Transwell method; and the expression of extracellular signal-regulated kinase (ERK) and EMT-related proteins in vivo and in vitro were detected by Western blotting. In addition, we established a lung metastasis model in nude mice using A549 cells to assess the effect of Rh3 on NSCLC tumor metastasis in vivo. Our findings suggest that Rh3 significantly inhibited lung cancer metastasis both in vivo and in vitro. It was determined by flow cytometry analysis that Rh3 notably inhibited cell proliferation by blocking the G1 phase. In addition, Rh3 inhibited metastasis in lung cancer cells and regulated the expression of metastasis-related proteins under hypoxia. Mechanistic studies suggested that Rh3 targeted ERK to inhibit lung cancer metastasis. The ERK inhibitor U0126 or siRNA-mediated knockdown of ERK had an enhanced effect on Rh3’s ability to inhibit lung cancer metastasis. The studies revealed that the inhibitory effect of Rh3 on the metastatic ability of lung cancer cells may be supported by ERK-related signaling pathways.
Sensing of Arbitrary-Frequency Fields Using a Quantum Mixer

Quantum sensors such as spin defects in diamond have achieved excellent performance by combining high sensitivity with spatial resolution. Unfortunately, these sensors can only detect signal fields with frequency in a few accessible ranges, typically low frequencies up to the experimentally achievable control field amplitudes and a narrow window around the sensors ’ resonance frequency. Here, we develop and demonstrate a technique for sensing arbitrary-frequency signals by using the sensor qubit as a quantum frequency mixer, enabling a variety of sensing applications. The technique leverages nonlinear effects in periodically driven (Floquet) quantum systems to achieve quantum frequency mixing of the signal and an applied bias ac field. The frequency-mixed field can be detected using well-developed sensing techniques such as Rabi and CPMG with the only additional requirement of the bias field. We further show that the frequency mixing can distinguish vectorial components of an oscillating signal field, thus enabling arbitrary-frequency vector magnetometry. We experimentally demonstrate this protocol with nitrogen-vacancy centers in diamond to sense a 150-MHz signal field, proving the versatility of the quantum mixer sensing technique.
Bone Tissue Engineering through 3D Bioprinting of Bioceramic Scaffolds: A Review and Update

Trauma and bone loss from infections, tumors, and congenital diseases make bone repair and regeneration the greatest challenges in orthopedic, craniofacial, and plastic surgeries. The shortage of donors, intrinsic limitations, and complications in transplantation have led to more focus and interest in regenerative medicine. Structures that closely mimic bone tissue can be produced by this unique technology. The steady development of three-dimensional (3D)-printed bone tissue engineering scaffold therapy has played an important role in achieving the desired goal. Bioceramic scaffolds are widely studied and appear to be the most promising solution. In addition, 3D printing technology can simulate mechanical and biological surface properties and print with high precision complex internal and external structures to match their functional properties. Inkjet, extrusion, and light-based 3D printing are among the rapidly advancing bone bioprinting technologies. Furthermore, stem cell therapy has recently shown an important role in this field, although large tissue defects are difficult to fill by injection alone. The combination of 3D-printed bone tissue engineering scaffolds with stem cells has shown very promising results. Therefore, biocompatible artificial tissue engineering with living cells is the key element required for clinical applications where there is a high demand for bone defect repair. Furthermore, the emergence of various advanced manufacturing technologies has made the form of biomaterials and their functions, composition, and structure more diversified, and manifold. The importance of this article lies in that it aims to briefly review the main principles and characteristics of the currently available methods in orthopedic bioprinting technology to prepare bioceramic scaffolds, and finally discuss the challenges and prospects for applications in this promising and vital field.