Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards CMOS Nuclear Magnetic Resonance Spectroscopy: Design, Implementation and Experimental Results

ii Dedication . iii Acknowledgments. iv TABLE OF CONTENTS . v LIST OF TABLES . vii LIST OF FIGURES . viii LIST OF ABBREVIATIONS . xi LIST OF SYMBOLS . xiii CHAPTER
Parental preference for influenza vaccine for children in China: a discrete choice experiment

Objectives To investigate what factors affect parents’ influenza vaccination preference for their children and whether there exists preference heterogeneity among respondents in China. Design Cross-sectional study. A discrete choice experiment was conducted. Five attributes were identified based on literature review and qualitative interviews, including protection rate, duration of vaccine-induced protection, risk of serious side effects, location of manufacturer and out-of-pocket cost. Setting Multistage sampling design was used. According to geographical location and the level of economic development, 10 provinces in China were selected, and the survey was conducted at community healthcare centres or stations. Participants Parents with at least one child aged between 6 months and 5 years old were recruited and the survey was conducted via a face-to-face interview in 2019. In total, 600 parents completed the survey, and 449 who passed the internal consistency test were included in the main analysis. Main outcomes and measures A mixed logit model was used to estimate factors affecting parents’ preference to vaccinate their children. In addition, sociodemographic characteristics were included to explore the preference heterogeneity. Results In general, respondents preferred to vaccinate their children. All attributes were statistically significant and among them, the risk of severe side effects was the most important attribute, followed by the protection rate and duration of vaccine-induced protection. Contrary to our initial expectation, respondents have a stronger preference for the domestic than the imported vaccine. Some preference heterogeneity among parents was also found and in particular, parents who were older, or highly educated placed a higher weight on a higher protection rate. Conclusion Vaccination safety and vaccine effectiveness are the two most important characteristics that influenced parents’ decision to vaccinate against influenza for their children in China. Results from this study will facilitate future policy implementations to improve vaccination uptake rates.
PARP Inhibitors Rising as an Epoch-Making Strategy in First-Line Maintenance Therapy of Ovarian Cancer: A Systematic Review and Meta-Analysis

Abstract Background To illustrate the accurate location of Poly-ADP-ribose polymerase inhibitor (PARPi) as the first-line maintenance therapy in advanced ovarian cancer (AOC). Methods Search for eligible studies and calculate clinical outcomes. Results PARPi as a first-line maintenance treatment significantly prolonged the BRCAmut population and the homologous recombination deficiency (HRD) positive population. Conclusion PARPi as first-line maintenance therapy significantly improves the progression-free survival in AOC, especially in the BRCAmut and HRD positive populations. PARPi has been becoming the standard first-line maintenance therapy for AOC.
Potential Anti-Inflammatory and Anti-Coagulation Effects of One-Time Application of Remote Ischemic Conditioning in Patients With Subacute/Chronic Cerebral Arteriostenosis and Venostenosis

Background: Remote ischemic conditioning (RIC) is an extremely simple, non-invasive, and cost-effective method with a neuroprotective effect. This study aimed to evaluate the immediate effects of one-time application of RIC on inflammation and coagulation in patients with chronic cerebral vascular stenosis, and compare the different effects of RIC on cerebral arteriostenosis and cerebral venostenosis. Method: A total of 47 patients with defined cerebral arteriostenosis (n=21) or venostenosis (n=26) were prospectively enrolled. RIC intervention was given once with 5 cycles of inflating and deflating for 5 minutes alternately. Blood was sampled 5 minutes before and after RIC for inflammatory and thrombophilia biomarkers. Differences in inflammatory and thrombotic variables at differing time points in the group were assessed using paired t tests or Wilcoxon matched-pairs signed-rank test. Results: Patients with cerebral arteriostenosis had a higher level of pre-RIC neutrophil-to-lymphocyte ratio (P=0.034), high-sensitivity C-reactive protein (P=0.037), and fibrinogen (P=0.002) than that with cerebral venostenosis. In the arterial group, levels of fibrinogen (P=0.023) decreased, and interleukin-6 levels were elevated (P=0.019) after a single RIC. Age was negatively related to interleukin-6, C-reactive protein, and fibrinogen. Conclusion: One-time RIC interventions may show seemingly coexisted proinflammatory and anti-coagulation effects of a single bout on patients with cerebral arteriostenosis. Older age was a negative predictor for multiple biomarkers in the cerebral arteriostensosis group. The protective effect of RIC on cerebral venostenosis patients needs to be further studied in a larger sample size.
Use of benzodiazepines is the risk factor for infection in patients aged 80 years or older with diffuse large B-cell lymphoma: A single-institution retrospective study

Background The number of patients aged 80 years or older with diffuse large B-cell lymphoma (DLBCL) is increasing, and the incidence rate of the disease in this population group reaches up to 20%. The risk of infection is higher in older patients than in other patients. Although hypnotic drugs are frequently detected as potentially inappropriate medications, it is unclear whether hypnotic drugs affect the occurrence of infection during chemotherapy. Here, we investigated whether the use of hypnotic drugs is associated with infection during first-line chemotherapy in patients with diffuse large B-cell lymphoma (DLBCL) aged 80 years or older. Methods Japanese patients aged 80 years or older with diffuse large B-cell lymphoma who had received first-line chemotherapy at Fujita Health University Hospital from January 2005 to March 2020 were enrolled in this retrospective cohort study. The primary study outcome was the identification of the risk factor for infection during first-line chemotherapy. Results This study included 65 patients received first-line chemotherapy. The proportion of patients with National Comprehensive Cancer Network-international prognostic index ≥ 6 was higher in the infection group than in the non-infection group. The relative dose intensity of each anticancer drug (cyclophosphamide, adriamycin, and vincristine) and dose of prednisolone did not significantly differ between the two groups. Multivariate analysis showed that the use of benzodiazepines was a risk factor for infection (odds ratio, 4.131 [95% confidence interval: 1.225–13.94], P = 0.022). Conclusion DLBCL patients using benzodiazepines should be monitored for infection symptoms during chemotherapy.
NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification

Message passing has evolved as an effective tool for designing graph neural networks (GNNs). However, most existing methods for message passing simply sum or average all the neighboring features to update node representations. They are restricted by two problems: 1) lack of interpretability to identify node features significant to the prediction of GNNs and 2) feature overmixing that leads to the oversmoothing issue in capturing long-range dependencies and inability to handle graphs under heterophily or low homophily. In this article, we propose a node-level capsule graph neural network (NCGNN) to address these problems with an improved message passing scheme. Specifically, NCGNN represents nodes as groups of node-level capsules, in which each capsule extracts distinctive features of its corresponding node. For each node-level capsule, a novel dynamic routing procedure is developed to adaptively select appropriate capsules for aggregation from a subgraph identified by the designed graph filter. NCGNN aggregates only the advantageous capsules and restrains irrelevant messages to avoid overmixing features of interacting nodes. Therefore, it can relieve the oversmoothing issue and learn effective node representations over graphs with homophily or heterophily. Furthermore, our proposed message passing scheme is inherently interpretable and exempt from complex post hoc explanations, as the graph filter and the dynamic routing procedure identify a subset of node features that are most significant to the model prediction from the extracted subgraph. Extensive experiments on synthetic as well as real-world graphs demonstrate that NCGNN can well address the oversmoothing issue and produce better node representations for semisupervised node classification. It outperforms the state of the arts under both homophily and heterophily.
Quantitative MRI in Multiple Sclerosis: From Theory to Application

SUMMARY: Quantitative MR imaging techniques allow evaluating different aspects of brain microstructure, providing meaningful information about the pathophysiology of damage in CNS disorders. In the study of patients with MS, quantitative MR imaging techniques represent an invaluable tool for studying changes in myelin and iron content occurring in the context of inflammatory and neurodegenerative processes. In the first section of this review, we summarize the physics behind quantitative MR imaging, here defined as relaxometry and quantitative susceptibility mapping, and describe the neurobiological correlates of quantitative MR imaging findings. In the second section, we focus on quantitative MR imaging application in MS, reporting the main findings in both the gray and white matter compartments, separately addressing macroscopically damaged and normal-appearing parenchyma.
RNAi-Based Therapeutics and Novel RNA Bioengineering Technologies

RNA interference (RNAi) provides researchers with a versatile means to modulate target gene expression. The major forms of RNAi molecules, genome-derived microRNAs (miRNAs) and exogenous small interfering RNAs (siRNAs), converge into RNA-induced silencing complexes to achieve posttranscriptional gene regulation. RNAi has proven to be an adaptable and powerful therapeutic strategy where advancements in chemistry and pharmaceutics continue to bring RNAi-based drugs into the clinic. With four siRNA medications already approved by the US Food and Drug Administration (FDA), several RNAi-based therapeutics continue to advance to clinical trials with functions that closely resemble their endogenous counterparts. Although intended to enhance stability and improve efficacy, chemical modifications may increase risk of off-target effects by altering RNA structure, folding, and biologic activity away from their natural equivalents. Novel technologies in development today seek to use intact cells to yield true biologic RNAi agents that better represent the structures, stabilities, activities, and safety profiles of natural RNA molecules. In this review, we provide an examination of the mechanisms of action of endogenous miRNAs and exogenous siRNAs, the physiologic and pharmacokinetic barriers to therapeutic RNA delivery, and a summary of the chemical modifications and delivery platforms in use. We overview the pharmacology of the four FDA-approved siRNA medications (patisiran, givosiran, lumasiran, and inclisiran) as well as five siRNAs and several miRNA-based therapeutics currently in clinical trials. Furthermore, we discuss the direct expression and stable carrier-based, in vivo production of novel biologic RNAi agents for research and development. SIGNIFICANCE STATEMENT In our review, we summarize the major concepts of RNA interference (RNAi), molecular mechanisms, and current state and challenges of RNAi drug development. We focus our discussion on the pharmacology of US Food and Drug Administration-approved RNAi medications and those siRNAs and miRNA-based therapeutics that entered the clinical investigations. Novel approaches to producing new true biological RNAi molecules for research and development are highlighted.
Microstructure, mechanical strength, chemical resistance, and antibacterial behavior of Ti–5Cu–x%Nb biomedical alloy

Titanium-based biomedical alloys are susceptible as they are used as a substitute for human bone. In this study, titanium alloy, Ti–5Cu–x%Nb (x = 0, 5, 10, 15) (%wt) was developed by powder metallurgy route. The effect of alloying niobium with Ti–5Cu alloy and its effect on the microstructure, mechanical strength, corrosion resistance, and antibacterial properties have been evaluated. The results show that the sintered alloy has both α-Ti and Ti2Cu phases. With increasing niobium content in the alloy, β-Ti was also detected. Additionally, it was found that the micro-hardness and compressive strength of the studied alloy was better than commercially pure titanium (cpTi), while the Young’s modulus was lower than cpTi. These properties are highly favorable for using this alloy to replicate the human cortical bone. The alloy was also tested for anticorrosive property in Ringer’s solution. The antibacterial activity was also examined for Staphylococcus aureus and Escherichia coli bacteria. The alloy showed promising anticorrosive and antibacterial ability.
Peruanocotyle pelagica n. sp. (Monogenea: Monocotylidae), Parasite of the Pacific Cownose Ray Rhinoptera steindachneri Evermann and Jenkins, 1891 (Batoidea: Rhinopteridae) from the Southern Mexican Pacific Ocean

ABSTRACT Peruanocotyle pelagica n. sp. is described based on specimens collected from the wall of the pharyngeal cavity of the Pacific cownose ray Rhinoptera steindachneri offshore Oaxaca and Guerrero, Mexico. The new species is distinguished from Peruanocotyle chisholmae by its anchors, which include a slender guard that curves towards the tip of the blade and which lack an accessory piece, morphological differences of the seminal vesicle, the lack of a male copulatory organ accessory piece and a greater number of spines, and an unsclerotized vagina. Molecular data of Peruanocotyle pelagica were generated to place the phylogenetic position of the genus within Monocotylidae.
Bridge or Barrier? The Impact of Network Capital on the Receipt of Long-Term Care Services in Germany

Objectives In Germany, roughly 8.3 million people are in need of care, but only one-third of them receive state care benefits. The study investigates whether the individual network of a care-seeking person, as well as its resources, interact with health status on the likelihood of accessing formal care services. Methods German data from the Survey of Health, Ageing and Retirement in Europe (SHARE) from 2015 were used in several Firth logistic regressions with interaction terms. Results Health limitations are a significant predictor for the probability of receiving formal care benefits. As moderating factors, caregivers from the immediate family as well as caregivers with lower levels of education tend to contribute to an increase in this probability. Discussion Findings are based on a limited data set and indicate the importance of further research in this area to examine the mechanisms of access to formal care more precisely.
Brain Tumor Cell Membrane‐Coated Lanthanide‐Doped Nanoparticles for NIR‐IIb Luminescence Imaging and Surgical Navigation of Glioma

Intraoperative visualization of the full extent of brain tumor by luminescence imaging helps to improve the degree and accuracy of brain tumor resection, thereby prolonging the survival of patients. However, the limited imaging depth and spatial resolution and the poor blood‐brain barrier (BBB) permeability of most currently available luminescent probes restrict the imaging performance and surgical resection efficiency of brain tumor. Here, a brain tumor cell membrane‐coated lanthanide‐doped nanoparticles (CC‐LnNPs) in the near‐infrared‐IIb window (NIR‐IIb, 1500–1700 nm) is designed for brain tumor imaging and surgical navigation. The coating of brain tumor cell membrane endows CC‐LnNPs with immune escape, BBB crossing, and homotypic targeting abilities, which are inherited from the source brain tumor cells. In addition, compared with clinically approved imaging agent indocyanine green, CC‐LnNPs present higher temporal and spatial resolution, higher stability, and lower background signals, enabling clear visualization of the brain tumor boundary. With the guidance of NIR‐IIb fluorescence, the glioma tissue (size < 3 mm, depth > 3 mm) could be clearly visualized and completely removed as a proof of concept. This study offers new insight for the future design of nanoprobe to image brain tumor and to achieve precise diagnosis and surgical navigation of brain tumor.
Characterization of the O/ME‐SA/Ind‐2001d foot‐and‐mouth disease virus epidemic recorded in the Maghreb during 2014–2015

Abstract The O/ME‐SA/Ind‐2001d has been the main foot‐and‐mouth disease virus (FMDV) lineage responsible for FMD epidemics outside the Indian subcontinent from 2013 to 2017. In 2014, outbreaks caused by this FMDV lineage were reported in Maghreb, where it was initially detected in Algeria and Tunisia and later in Morocco. This was the first incursion of an FMDV type O of exotic origin in the Maghreb region after 14 years of absence. In this study, we report analyses of both VP1 and whole‐genome sequences (WGSs) generated from 22 isolates collected in Algeria and Tunisia between 2014 and 2015. All the WGSs analysed showed a minimum pairwise identity of 98.9% at the nucleotide level and 99% at the amino acid level (FMDV coding region). All Tunisian sequences shared a single putative common ancestor closely related to FMDV strains circulating in Libya during 2013. Whereas sequences from Algeria suggest the country experienced two virus introductions. The first introduction is represented by strains circulating in 2014 which are closely related to those from Tunisia, the second one, of which the origin is more uncertain, includes strains collected in Algeria in 2015 that gave origin to the 2015 outbreak reported in Morocco. Overall, our results demonstrated that a unique introduction of O/Ind‐2001d FMDV occurred in Maghreb through Tunisia presumably in 2014, and from then the virus spread into Algeria and later into Morocco.
Condition monitoring of engine lubrication oil of military vehicles: a machine learning approach

Lubrication oil plays an important role in maintaining the health and performance of a land vehicle engine. Accurate condition monitoring of lubrication oil enables an effective predictive maintenance regime to be established. This can extend engine life as well as reduce over or under-servicing and other unnecessary maintenance costs. Machine learning models are useful for mining meaningful patterns from data samples. In this research, through the application of such models, we classify the condition of engine lubrication oil based on data from the Vehicle Health and Usage Monitoring System and laboratory test results of lubrication oil from a cohort of military land vehicles. The oil condition is classified into three categories: normal, degraded, and unsuitable. Feature selection methods are used to identify the best feature set for representing the lubrication oil condition. Importantly, the machine learning models employed provide the predicted output with justification in the form of explanatory rules pertaining to the lubrication oil condition. The findings indicate that (i) a good feature selection method is necessary to reduce the dimensionality of the feature set used for classification; (ii) machine learning provides a viable method for classifying oil condition with understandable justifications.
Model Fusion and Multiscale Feature Learning for Fault Diagnosis of Industrial Processes

The data generated by modern industrial processes often exhibit high-dimensional, nonlinear, timing, and multiscale characteristics. Presently, most of the fault diagnosis methods based on deep learning only consider the part of the characteristics of industrial data, which will cause the loss of part of the feature information during training, thereby affecting the final diagnosis effect. In order to solve the above problems, this article proposes an end-to-end multiscale feature learning method based on model fusion, which can simultaneously extract multiscale spatial features and temporal features of data, effectively reducing the loss of feature information. First, this article combines the convolutional neural network (CNN) with residual learning and designs a multiscale residual network (MRCNN) to extract high-dimensional nonlinear spatial features of different scales in the data. Then, the extracted features are input into the long and short-term memory (LSTM) network to further extract the temporal features of the data. After the fully connected layer, it is input into the classifier for final fault classification. The residual learning in MRCNN can effectively avoid the problem of model degradation and improve the training efficiency of the model. Through the fusion of MRCNN and LSTM, we can significantly improve the feature extraction ability of the model, thereby greatly improving the diagnosis effect. In the final case experiment, the method improved the comprehensive diagnostic accuracy of the Tennessee-Eastman (TE) process and industrial coking furnace datasets to 94.43% and 97.80%, respectively, which was significantly better than the existing deep learning model and proves the effectiveness and superiority of this method.
Early Termination Based Training Acceleration for an Energy-Efficient SNN Processor Design

In this paper, we present a novel early termination based training acceleration technique for temporal coding based spiking neural network (SNN) processor design. The proposed early termination scheme can efficiently identify the non-contributing training images during the training's feedforward process, and it skips the rest of the processes to save training energy and time. A metric to evaluate each input image's contribution to training has been developed, and it is compared with pre-determined threshold to decide whether to skip the rest of the training process. For the threshold selection, an adaptive threshold calculation method is presented to increase the computation skip ratio without sacrificing accuracy. Timestep splitting approach is also employed to allow more frequent early termination in split timesteps, thus leading to more computation savings. The proposed early termination and timestep splitting techniques achieve 51.21/42.31/93.53/30.36% reduction of synaptic operations and 86.06/64.63/90.82/49.14% reduction of feedforward timestep for the training process on MNIST/Fashion-MNIST/ETH-80/EMNIST-Letters dataset, respectively. The hardware implementation of the proposed SNN processor using 28 nm CMOS process shows that the SNN processor achieves the training energy saving of 61.76/31.88% and computation cycle reduction of 69.10/36.26% on MNIST/Fashion-MNIST dataset, respectively.