Miracle-dz/UniFAR-FTUs huggingface dataset:
Combination Therapy with Oseltamivir and Favipiravir Delays Mortality but Does Not Prevent Oseltamivir Resistance in Immunodeficient Mice Infected with Pandemic A(H1N1) Influenza Virus

Immunosuppressed individuals can shed influenza virus for prolonged periods of time, leading to the frequent emergence of antiviral resistance. We evaluated the benefits of oseltamivir and favipiravir combination therapy compared to single antiviral agents and monitored the emergence of drug-resistant variants in a pharmacologically immunosuppressed mouse model infected with the A(H1N1) pandemic influenza virus. C57BL/6 mice were immunosuppressed with cyclophosphamide and infected with a lethal dose of pandemic influenza A(H1N1) virus. Forty-eight hours post-infection, mice were treated with oseltamivir (20 mg/kg), favipiravir (20 or 50 mg/kg) or both agents BID for 5 or 10 days. Body weight losses, survival rates, lung viral titers, cytokine levels and emergence of resistant viruses were evaluated. Treatment of immunosuppressed mice with high (50 mg/kg) but not low (20 mg/kg) doses of favipiravir in combination with oseltamivir (20 mg/kg) significantly delayed mortality and reduced lung viral titers compared to treatment with a single drug regimen with oseltamivir but did not prevent the emergence of oseltamivir-resistant H275Y neuraminidase variants. Combination therapy with oseltamivir and favipiravir should be considered for evaluation in clinical trials.
Protective Effects of Dioscorea batatas Flesh and Peel Extracts against Ethanol-Induced Gastric Ulcer in Mice

Gastric ulcer is a major digestive disorder and provoked by multifactorial etiologies, including excessive alcohol consumption. In this study, we examined the gastroprotective effect of aqueous and ethanolic extracts of Dioscorea batatas Decne (DBD; commonly called Chinese yam) flesh or peel against acidified ethanol-induced acute gastric damage in mice. Our findings demonstrated that oral supplementation of aqueous or ethanolic extracts of DBD flesh or peel before ulcer induction was significantly effective in macroscopically and histologically alleviating ethanol-induced pathological lesions in gastric mucosa, decreasing the plasma levels of inflammatory mediators, such as nitric oxide and interleukin-6, attenuating the gastric expression of cyclooxygenase-2, and increasing the gastric content of prostaglandin E2. In particular, pretreatment with the flesh extract prepared in 60% ethanol prominently decreased the expression of biomarkers of oxidative stress, including the plasma levels of 8-hydroxy-2-guanosine and malondialdehyde, and restored heme oxygenase-1 expression and superoxide dismutase activity in the stomach. Overall, these findings suggest that the oral supplementation with DBD extract, especially flesh ethanol extract, prior to excessive alcohol consumption, may exert a protective effect against ethanol-induced gastric mucosal damage in vivo, presumably through the activation of the antioxidant system and suppression of the inflammatory response.
Will Digital Fluency Influence Social Media Use?: An Empirical Study of WeChat Users

Social media has increasingly been used in both personal and professional communications. Past studies note that individuals of digital fluency present distinctive behaviors in technology use. However, limited research has explored how digital fluency affects the relationship between individual gratifications and social media use. Based on the uses and gratifications theory and digital fluency literatures, we develop a model to explore the relationships among digital fluency, gratifications, and social media use. We test the model using survey data from 262 users of WeChat, which is the most popular social media platform in China. Results show that digital fluency is positively associated with social media use. Moreover, we find that digital fluency negatively moderates the impacts of maintaining ties, enjoyment, and information need fulfillment on social media use, whereas it positively moderates the impact of broadening social network. Our findings contribute to the literature by identifying the theoretical and practical roles of digital fluency in social media use.
Very High Resolution Image Scene Classification with Semantic Fisher Vectors

Very high resolution (VHR) image scene classification is the most challenging of remote sensing data analysis, that has attracted researchers' attention. To improve the precision of VHR image scene classification, we propose a new method based on convolutional features extracted by convolutional neural network (CNN). First, Visual Geometry Group Network (VGG-Net) model is introduced as a feature extractor form the original VHR images. Second, we select the fifth convolutional layer constructed by VGG-Net, which is supposed as convolutional features descriptors. Third, based on Improved Fisher Vector (IFV) coding method, we compute the visual word corresponding to the convolutional features of the image scene. We conduct experiments on the public AID benchmark dataset, which contains 30 different areal categories with sub-meter resolution. Experimental results demonstrate the effectiveness of the proposed method, as compared with several state-of-the-art methods.
Analysis of Interaction Between Interfacial Structure and Fibrinogen at Blood-Compatible Polymer/Water Interface

The correlation between the interfacial structure and protein adsorption at a polymer/water interface was investigated. Poly(2-methoxyethyl acrylate)(PMEA), which is one of the best blood compatible polymers available, was employed. Nanometer-scale structures generated through the phase separation of polymer and water were observed at the PMEA/phosphate buffered saline interface. The interaction between the interfacial structures and fibrinogen (FNG) was measured using atomic force microscopy. Attraction was observed in the polymer-rich domains as well as in the non-blood compatible polymer. In contrast, no attractive interactions were observed, and only a repulsion occurred in the water-rich domains. The non-adsorption of FNG into the water rich domains was also clarified through topographic and phase image analyses. Furthermore, the FNG molecules adsorbed on the surface of PMEA were easily desorbed, even in the polymer-rich domains. Water molecules in the water-rich domains are anticipated to be the dominant factor in preventing FNG adsorption and thrombogenesis on a PMEA interface.
High-level expression and molecular characterization of a recombinant prolidase from Escherichia coli NovaBlue

Long-term use of organophosphorus (OP) compounds has become an increasing global problem and a major threat to sustainability and human health. Prolidase is a proline-specific metallopeptidase that can offer an efficient option for the degradation of OP compounds. In this study, a full-length gene from Escherichia coli NovaBlue encoding a prolidase (EcPepQ) was amplified and cloned into the commercially-available vector pQE-30 to yield pQE-EcPepQ. The overexpressed enzyme was purified from the cell-free extract of isopropyl thio-β-D-galactoside IPTG-induced E. coli M15 (pQE-EcPepQ) cells by nickel-chelate chromatography. The molecular mass of EcPepQ was determined to be about 57 kDa by 12% sodium dodecyl sulfate–polyacrylamide gel electrophoresis and the result of size-exclusion chromatography demonstrated that the enzyme was mainly present in 25 mM Tris–HCl buffer (pH 8.0) as a dimeric form. The optimal conditions for EcPepQ activity were 60 °C, pH 8.0, and 0.1 mM Mn2+ ion. Kinetic analysis with Ala-Pro as the substrate showed that the Km and kcat values of EcPepQ were 8.8 mM and 926.5 ± 2.0 s−1, respectively. The thermal unfolding of EcPepQ followed a two-state process with one well-defined unfolding transition of 64.2 °C. Analysis of guanidine hydrochloride (GdnHCl)-induced denaturation by tryptophan emission fluorescence spectroscopy revealed that the enzyme had a [GdnHCl]0.5,N-U value of 1.98 M. The purified enzyme also exhibited some degree of tolerance to various water/organic co-solvents. Isopropanol and tetrahydrofuran were very detrimental to the enzymatic activity of EcPepQ; however, other more hydrophilic co-solvents, such as formamide, methanol, and ethylene glycol, were better tolerated. Eventually, the non-negative influence of some co-solvents on both catalytic activity and structural stability of EcPepQ allows to adjust the reaction conditions more suitable for EcPepQ-catalyzed bioprocess.
Two-Year Longitudinal Monitoring of Amnestic Mild Cognitive Impairment Patients with Prodromal Alzheimer's Disease Using Topographical Biomarkers Derived from Functional Magnetic Resonance Imaging and Electroencephalographic Activity.

Auditory "oddball" event-related potentials (aoERPs), resting state functional magnetic resonance imaging (rsfMRI) connectivity, and electroencephalographic (rsEEG) rhythms were tested as longitudinal functional biomarkers of prodromal Alzheimer's disease (AD). Data were collected at baseline and four follow-ups at 6, 12, 18, and 24 months in amnesic mild cognitive impairment (aMCI) patients classified in two groups: "positive" (i.e., "prodromal AD"; n = 81) or "negative" (n = 63) based on a diagnostic marker of AD derived from cerebrospinal samples (Aβ42/P-tau ratio). A linear mixed model design was used to test functional biomarkers for Group, Time, and Group×Time effects adjusted by nuisance covariates (only data until conversion to dementia was used). Functional biomarkers that showed significant Group effects ("positive" versus "negative", p < 0.05) regardless of Time were 1) reduced rsfMRI connectivity in both the default mode network (DMN) and the posterior cingulate cortex (PCC), both also giving significant Time effects (connectivity decay regardless of Group); 2) increased rsEEG source activity at delta (<4 Hz) and theta (4-8 Hz) rhythms and decreased source activity at low-frequency alpha (8-10.5 Hz) rhythms; and 3) reduced parietal and posterior cingulate source activities of aoERPs. Time×Group effects showed differential functional biomarker progression between groups: 1) increased rsfMRI connectivity in the left parietal cortex of the DMN nodes, consistent with compensatory effects and 2) increased limbic source activity at theta rhythms. These findings represent the first longitudinal characterization of functional biomarkers of prodromal AD relative to "negative" aMCI patients based on 5 serial recording sessions over 2 years.
COSMO Skymed Images for the Monitoring of Cryosphere in Alpine Areas

In this work, the characterization and extraction of snowpack parameters from X-band SAR imagery is investigated. X-band is not yet the most suitable frequency for the retrieval of snow parameters, namely Snow Depth (SD) and Snow Water Equivalent (SWE); however, it is the higher frequency available in the existing SAR systems. Previous research demonstrated that retrieval of SD/SWE can be achieved at this frequency too for $\mathrm{SWE} > 100-150\mathrm{mm}$. A sensitivity analysis is carried out by exploiting datasets of in situ measurements (snow depth, density, snow grain radius, temperature and wetness) collected on two test sites in the Eastern Italian Alps: the Cordevole plateau (Dolomites) and the Ulten valley (South Tyrol). This analysis provides indications on the sensitivity of X band backscattering to the target snow parameters. As a second step of the analysis, simulations based on a forward electromagnetic model, namely the Dense Medium Radiative Transfer (DMRT), are considered for interpreting and assessing the experimental findings. Finally, an attempt of implementing a retrieval algorithm for estimating SWE from X-band data is carried out. The algorithm is based on machine learning approaches, namely Supported Vector Regressions (SVR) and Artificial Neural Networks (ANN). The training of both algorithms accounts for experimental data and DMRT model simulations. These algorithms are applied to time series of COSMO-SkyMed (CSK) images collected on both test areas. The obtained results are encouraging, although more analysis and validation is needed for exploiting the potential of SAR X band in snow parameter retrieval.
Artificial intelligence and echocardiography

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome.
A Proposed Roadmap for Parkinson’s Disease Proof of Concept Clinical Trials Investigating Compounds Targeting Alpha-Synuclein

The convergence of human molecular genetics and Lewy pathology of Parkinson’s disease (PD) have led to a robust, clinical-stage pipeline of alpha-synuclein (α-syn)-targeted therapies that have the potential to slow or stop the progression of PD and other synucleinopathies. To facilitate the development of these and earlier stage investigational molecules, the Michael J. Fox Foundation for Parkinson’s Research convened a group of leaders in the field of PD research from academia and industry, the Alpha-Synuclein Clinical Path Working Group. This group set out to develop recommendations on preclinical and clinical research that can de-risk the development of α-syn targeting therapies. This consensus white paper provides a translational framework, from the selection of animal models and associated end-points to decision-driving biomarkers as well as considerations for the design of clinical proof-of-concept studies. It also identifies current gaps in our biomarker toolkit and the status of the discovery and validation of α-syn-associated biomarkers that could help fill these gaps. Further, it highlights the importance of the emerging digital technology to supplement the capture and monitoring of clinical outcomes. Although the development of disease-modifying therapies targeting α-syn face profound challenges, we remain optimistic that meaningful strides will be made soon toward the identification and approval of disease-modifying therapeutics targeting α-syn.
Food consumption and the risk of childhood allergy

Background The prevalence of allergic conditions is increasing in most countries. One possible explanation may be childhood nutrition. Objective The aim of the study was to investigate the relationship between consumption of pre-specified types of food in school-aged children and presence of respiratory and allergic symptoms. Methods A total of 191 students aged 9–11 years were recruited from 5 schools to geographically cover all of Malta. Data was collected between October 2011 and February 2012. This was part of a bigger study which included clinical and environmental tests besides standardized health questionnaires. For the purposes of this part of the study only the health questionnaires were used. These standardized health questionnaires based on the International Study of Asthma and Allergies in Childhood (ISAAC) were used to identify the presence of respiratory and allergic symptoms and to identify the types of foods and the frequency of consumption of various types of foods. Results We found that milk, meat, butter, olive oil, and yoghurt consumption had a negative association with allergic symptoms in children, whilst fish consumption had a detrimental effect. These relationships remained significant after correction for paternal level of education. Conclusion The study highlights the fact that nutrition in early childhood may have a significant effect on the risk of allergic conditions. Our results, taken together with data in the literature, suggest that different types of fish might have had different effects. This is probably related to their different fatty acid constitution thus warranting further studies.
Constrained Nonnegative Matrix Factorization for Robust Hyperspectral Unmixing

Hyperspectral unmixng (HU) is an essential step for hyperspectral image (HSI) analysis. In real HSI, there often are abnormal fluctuations existing in specific bands, which can be described as sparse noise. This type of corruption will seriously disrupt the hyperspectral image quality, causing extra difficulties during unmixing process. However, the influence of sparse noise is often ignored by existing unmixing methods, which leads to the reduction of robustness and accuracy for HU tasks. Therefore, we propose a new unmixing model which takes noise corruption into consideration. By designing and imposing constraints considering the sparsity of noise, properties of endmember and abundance on nonnegative matrix factorization (NMF), the proposed method can resist the sparse noise and achieve more robust and accurate unmixing results. Adequate experiments have been conducted on both synthetic and real hyperspectral data. And the results confirm the superiority of proposed method compared to state-of-the-art methods.
Possibilities and Limits of Urban Change Detection Using Polarimetric SAR Data

This paper introduces the topic of urban change detection by means of fully and dual polarized multitemporal spaceborne SAR data. The aim is to be able to detect not only urban extent expansion/shrinking phenomena, but changes that may affect both the two- and the three-dimensional landscape. For fully polarized SAR, we further validate and refine a recently introduced approach based on superpixel segmentation followed by the computation of selected polarimetric parameters and the analysis of the difference among feature vectors using specialized distance metrics. As for dual polarized SAR data, a combination of interferometric and polarimetric features within the same framework is proved to perform decently, but no real advantage if using polarimetry is obtained. Test cases using UAVSAR and Sentinel-l data are presented in support of the proposed techniques.
Heavy Metal Mixture Exposure and Effects in Developing Nations: An Update

The drive for development and modernization has come at great cost. Various human activities in developed and developing countries, particularly in sub-Saharan Africa (SSA) have given rise to environmental safety concerns. Increased artisanal mining activities, illegal refining, use of leaded petrol, airborne dust, arbitrary discarding and burning of toxic waste, absorption of production industries in inhabited areas, inadequate environmental legislation, and weak implementation of policies, have given rise to the incomparable contamination and pollution associated with heavy metals in recent decades. This review evaluates the public health effects of heavy metals and their mixtures in SSA. This shows the extent and size of the problem posed by exposure to heavy metal mixtures in regard to public health.
Modelling of Near-Surface Soil Moisture Using Machine Learning and Multi-Temporal Sentinel 1 Images in New Zealand

The improved spatio-temporal resolution of the C-band Sentinel 1 Synthetic Aperture Radar (SAR) mission has increased the number of possibilities in soil moisture retrieval studies. In this paper, an ensemble learning method (Random Forest) was applied on a group of data acquired on a farm scale in New Zealand's hill country. A statistical model was fitted to predict volumetric soil moisture (θv, %) from radar data, the degree of vegetation coverage represented by NDVI and a number of landscape parameters. The model performance demonstrated that the algorithm was able to capture the non-linear relationship between the ground-based and remotely sensed variables. θv predictions using a time series of radar images reached an average accuracy of 3% for RMSE and 0.86 for R2. An extended version of the proposed method has the potential to be a basis of more accurate water balance simulations, applied in the hydrologically complex pastoral hill country.
Long-Lasting Effects of Methocinnamox on Opioid Self-Administration in Rhesus Monkeys

Opioid abuse remains a serious public health challenge, despite the availability of medications that are effective in some patients (naltrexone, buprenorphine, and methadone). This study explored the potential of a pseudoirreversible mu-opioid receptor antagonist [methocinnamox (MCAM)] as a treatment for opioid abuse by examining its capacity to attenuate the reinforcing effects of mu-opioid receptor agonists in rhesus monkeys. In one experiment, monkeys responded for heroin (n = 5) or cocaine (n = 4) under a fixed-ratio schedule. Another group (n = 3) worked under a choice procedure with one alternative delivering food and the other alternative delivering the mu-opioid receptor agonist remifentanil. A third group (n = 4) responded for food and physiologic parameters were measured via telemetry. The effects of MCAM were determined in all experiments and, in some cases, were compared with those of naltrexone. When given immediately before sessions, naltrexone dose-dependently decreased responding for heroin and decreased choice of remifentanil while increasing choice of food, with responding returning to baseline levels 1 day after naltrexone injection. MCAM also decreased responding for heroin and decreased choice of remifentanil while increasing choice of food; however, opioid-maintained responding remained decreased for several days after treatment. Doses of MCAM that significantly decreased opioid-maintained responding did not decrease responding for cocaine or food. MCAM did not impact heart rate, blood pressure, body temperature, or activity at doses that decreased opioid self-administration. Because MCAM selectively attenuates opioid self-administration for prolonged periods, this novel drug could be a safe and effective alternative to currently available treatments for opioid abuse.