Miracle-dz/UniFAR-FTUs huggingface dataset:
Insecticidal and Repellent Activity of Thymus quinquecostatus Celak. Essential Oil and Major Compositions against Three Stored‐Product Insects

Thymus quinquecostatus Celak. of the Lamiaceae family has a long history of dual use of medicine and food with high economic value, and has been proved to have good antioxidative, antimicrobial, and antidiabetic activities. Essential oil (EO) extracted from the aerial part of T. quinquecostatus was obtained by hydrodistillation and analyzed by gas chromatography‐mass spectrometry (GC/MS) and GC. Totally 45 compounds were identified accounting for 98.5 % of the EO. The major constituents of the EO were linalool (52.003 %), borneol (10.911 %) and anethole (5.325 %). Fumigant, contact and repellent activity of T. quinquecostatus essential oil (EO) against Tribolium castaneum (Coleoptera: Tenebrionidae), Lasioderma Serricorne (Coleoptera: Anobiidae), Liposcelis bostrychophila (Psocoptera: Liposcelididae) were evaluated in this work. T. quinquecostatus EO and three major constituents showed varying degrees of insecticidal and repellent activities to three stored‐product insects. Anethole stated the best fumigant and contact activity than others did to all three insect species. EO and compounds showed general repellent activity against three target insects. This work suggests that the EO of T. quinquecostatus has promising potential to be developed into botanical pesticides and repellents controlling pest damage in warehouses and grain stores.
Alnus glutinosa Threatened by Alder Phytophthora: A Histological Study of Roots

Alder dieback remains a major problem in European alder stands and its spread continues to threaten their existence. The causal agent of this disease is the so-called alder Phytophthora species complex, which includes the hybrid Phytophthora ×alni and its parental species P. uniformis and P. ×multiformis. Little is known about the survival of these Phytophthora species in alder. The aim of our investigations was to find out whether, and if so where, the pathogen survives. The subject of these studies was alder roots. Therefore, artificial infection studies and histological studies with P. ×alni and P. uniformis were carried out on seedlings of black alder (Alnus glutinosa). These histological studies revealed oogonia and oospores of P. ×alni and P. uniformis in different parts of the root tissue.
Should We Embed in Chemistry? A Comparison of Unsupervised Transfer Learning with PCA, UMAP, and VAE on Molecular Fingerprints

Methods for dimensionality reduction are showing significant contributions to knowledge generation in high-dimensional modeling scenarios throughout many disciplines. By achieving a lower dimensional representation (also called embedding), fewer computing resources are needed in downstream machine learning tasks, thus leading to a faster training time, lower complexity, and statistical flexibility. In this work, we investigate the utility of three prominent unsupervised embedding techniques (principal component analysis—PCA, uniform manifold approximation and projection—UMAP, and variational autoencoders—VAEs) for solving classification tasks in the domain of toxicology. To this end, we compare these embedding techniques against a set of molecular fingerprint-based models that do not utilize additional pre-preprocessing of features. Inspired by the success of transfer learning in several fields, we further study the performance of embedders when trained on an external dataset of chemical compounds. To gain a better understanding of their characteristics, we evaluate the embedders with different embedding dimensionalities, and with different sizes of the external dataset. Our findings show that the recently popularized UMAP approach can be utilized alongside known techniques such as PCA and VAE as a pre-compression technique in the toxicology domain. Nevertheless, the generative model of VAE shows an advantage in pre-compressing the data with respect to classification accuracy.
Hantavirus Replication Cycle—An Updated Structural Virology Perspective

Hantaviruses infect a wide range of hosts including insectivores and rodents and can also cause zoonotic infections in humans, which can lead to severe disease with possible fatal outcomes. Hantavirus outbreaks are usually linked to the population dynamics of the host animals and their habitats being in close proximity to humans, which is becoming increasingly important in a globalized world. Currently there is neither an approved vaccine nor a specific and effective antiviral treatment available for use in humans. Hantaviruses belong to the order Bunyavirales with a tri-segmented negative-sense RNA genome. They encode only five viral proteins and replicate and transcribe their genome in the cytoplasm of infected cells. However, many details of the viral amplification cycle are still unknown. In recent years, structural biology methods such as cryo-electron tomography, cryo-electron microscopy, and crystallography have contributed essentially to our understanding of virus entry by membrane fusion as well as genome encapsidation by the nucleoprotein. In this review, we provide an update on the hantavirus replication cycle with a special focus on structural virology aspects.
UK veterans’ mental health and well-being before and during the COVID-19 pandemic: a longitudinal cohort study

Objective To investigate the impact of the COVID-19 pandemic on the health and well-being of UK ex-service personnel (veterans) before and during the pandemic, and to assess associations of COVID-19 experiences and stressors with mental health, alcohol use and loneliness. Design An additional wave of data was collected from a longitudinal cohort study of the UK Armed Forces. Setting Online survey June–September 2020. Participants Cohort members were included if they had completed a questionnaire at phase 3 of the King’s Centre for Military Health Research health and well-being study (2014–2016), had left the Armed Forces after regular service, were living in the UK, had consented to follow-up and provided a valid email address. Invitation emails were sent to N=3547 with a 44% response rate (n=1562). Primary outcome measures Common mental health disorders (CMDs) (measured using the General Health Questionnaire, 12 items—cut-off ≥4), hazardous alcohol use (measured using the Alcohol Use Disorder Identification Test, 10 items—cut off ≥8) and loneliness (University of California, Los Angeles, Loneliness Scale— 3 items-cut-off ≥6). Results Veterans reported a statistically significant decrease in hazardous drinking of 48.5% to 27.6%, while CMD remained stable (non-statistically significant increase of 24.5% to 26.1%). 27.4% of veterans reported feelings of loneliness. The COVID-19 stressors of reporting difficulties with family/social relationships, boredom and difficulties with health were statistically significantly associated with CMD, hazardous drinking and loneliness, even after adjustment for previous mental health/hazardous alcohol use. Conclusions Our study suggests a COVID-19 impact on veterans’ mental health, alcohol use and loneliness, particularly for those experiencing difficulties with family relationships. Veterans experienced the pandemic in similar ways to the general population and in some cases may have responded in resilient ways. While stable levels of CMD and reduction in alcohol use are positive, there remains a group of veterans who may need mental health and alcohol treatment services.
Discrimination of the Essential Oils Obtained from Four Apiaceae Species Using Multivariate Analysis Based on the Chemical Compositions and Their Biological Activity

The chemical composition of the essential oils obtained from the aerial parts of four Apiaceae species, namely Elaeosticta allioides (EA), E. polycarpa (EP), Ferula clematidifolia (FC), and Hyalolaena intermedia (HI), were determined using gas chromatography. Altogether, 100 volatile metabolites representing 78.97, 81.03, 85.78, and 84.49% of the total components present in EA, EP, FC, and HI oils, respectively, were reported. allo-Ocimene (14.55%) was the major component in FC, followed by D-limonene (9.42%). However, in EA, germacrene D (16.09%) was present in a high amount, while heptanal (36.89%) was the predominant compound in HI. The gas chromatographic data were subjected to principal component analysis (PCA) to explore the correlations between these species. Fortunately, the PCA score plot could differentiate between the species and correlate Ferula to Elaeosticta species. Additionally, the antioxidant activity was evaluated in vitro using the 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH), 2,2-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), and the ferric reducing power (FRAP) assays. In addition, the antimicrobial activity using the agar diffusion method was assessed, and the minimum inhibitory concentrations (MICs) were determined. Furthermore, the cell viability MTT assay was performed to evaluate the cytotoxicity of the essential oils against hepatic (HepG-2) and cervical (HeLa) cancer cell lines. In the DPPH assay, FC exhibited the maximum activity against all the antioxidant assays with IC50 values of 19.8 and 23.0 μg/mL for the DPPH and ABTS assays, respectively. Ferula showed superior antimicrobial and cytotoxic activities as well. Finally, a partial least square regression model was constructed to predict the antioxidant capacity by utilizing the metabolite profiling data. The model showed excellent predictive ability by applying the ABTS assay.
Physiological Responses of Young Pea and Barley Seedlings to Plasma-Activated Water

This study demonstrates the indirect effects of non-thermal ambient air plasmas (NTP) on seed germination and plant growth. It investigates the effect of plasma-activated water (PAW) on 3-day-old seedlings of two important farm plants—barley and pea. Applying different types of PAW on pea seedlings exhibited stimulation of amylase activity and had no inhibition of seed germination, total protein concentration or protease activity. Moreover, PAW caused no or only moderate oxidative stress that was in most cases effectively alleviated by antioxidant enzymes and proved by in situ visualization of H2O2 and ˙O2−. In pea seedlings, we observed a faster turn-over from anaerobic to aerobic metabolism proved by inhibition of alcohol dehydrogenase (ADH) activity. Additionally, reactive oxygen/nitrogen species contained in PAW did not affect the DNA integrity. On the other hand, the high level of DNA damage in barley together with the reduced root and shoot length and amylase activity was attributed to the oxidative stress caused by PAW, which was exhibited by the enhanced activity of guaiacol peroxidase or ADH. Our results show the glow discharge PAW at 1 min activation time as the most promising for pea. However, determining the beneficial type of PAW for barley requires further investigation.
Genotyping of Porcine Circovirus 2 (PCV-2) in Vaccinated Pigs Suffering from PCV-2-Systemic Disease between 2009 and 2020 in Spain

Vaccination against porcine circovirus 2 (PCV-2) is a common practice all over the world. Vaccines can prevent PCV-2-systemic disease (PCV-2-SD) outbreaks but not PCV-2 infection, which can be detectable in a percentage of vaccinated animals. Occasionally, PCV-2-SD is diagnosed in vaccinated farms. The objective of this study was to genotype the PCV-2 strains detected in vaccinated animals diagnosed with PCV-2-SD. Additionally, the evolution of the frequency of PCV-2 genotype detection at Spanish, European, and world levels was assessed. Fifty cases diagnosed as PCV-2-SD between 2009 and 2020 were included in this study. PCV-2 genotype was determined by sequencing the Cap gene region. Among them, only PCV-2b (23/50, 46%) and PCV-2d (27/50, 54%) genotypes were detected. Although the frequency of detection of these two genotypes was similar, their temporal distribution was different. Whereas most PCV-2b sequences (17/23, 74%) were detected between 2009 and 2012, PCV-2d sequences were obtained from 2013 to 2020. Indeed, a predominance of the PCV-2d genotype was observed from 2013 onwards, a trend also noticed at European and world levels. The results suggest that detection of particular genotypes in vaccinated animals probably reflects the general prevalence of the genotypes over time rather than genotype-specific vaccine-immunity escaping.
A Lightweight Deep Network for Defect Detection of Insert Molding Based on X-ray Imaging

Aiming at the abnormality detection of industrial insert molding processes, a lightweight but effective deep network is developed based on X-ray images in this study. The captured digital radiography (DR) images are firstly fast guide filtered, and then a multi-task detection dataset is constructed using an overlap slice in order to improve the detection of tiny targets. The proposed network is extended from the one-stage target detection method of yolov5 to be applicable to DR defect detection. We adopt the embedded Ghost module to replace the standard convolution to further lighten the model for industrial implementation, and use the transformer module for spatial multi-headed attentional feature extraction to perform improvement on the network for the DR image defect detection. The performance of the proposed method is evaluated by consistent experiments with peer networks, including the classical two-stage method and the newest yolo series. Our method achieves a mAP of 93.6%, which exceeds the second best by 3%, with robustness sufficient to cope with luminance variations and blurred noise, and is more lightweight. We further conducted ablation experiments based on the proposed method to validate the 32% model size reduction owing to the Ghost module and the detection performance enhancing effect of other key modules. Finally, the usability of the proposed method is discussed, including an analysis of the common causes of the missed shots and suggestions for modification. Our proposed method contributes a good reference solution for the inspection of the insert molding process.
Comparison of the Physico-Mechanical and Weathering Properties of Wood–Plastic Composites Made of Wood Fibers from Discarded Parts of Pomelo Trees and Polypropylene

The purpose of this study is to compare the characteristics of wood–plastic composites (WPCs) made of polypropylene (PP) and wood fibers (WFs) from discarded stems, branches, and roots of pomelo trees. The results show that the WPCs made of 30–60 mesh WFs from stems have better physical, flexural, and tensile properties than other WPCs. However, the flexural strengths of all WPCs are not only comparable to those of commercial wood–PP composites but also meet the strength requirements of the Chinese National Standard for exterior WPCs. In addition, the color change of WPCs that contained branch WFs was lower than that of WPCs that contained stem or root WFs during the initial stage of the accelerated weathering test, but the surface color parameters of all WPCs were very similar after 500 h of xenon arc accelerated weathering. Scanning electron microscope (SEM) micrographs showed many cracks on the surfaces of WPCs after accelerated weathering for 500 h, but their flexural modulus of rupture (MOR) and modulus of elasticity (MOE) values did not differ significantly during weathering. Thus, all the discarded parts of pomelo trees can be used to manufacture WPCs, and there were no significant differences in their weathering properties during 500 h of xenon arc accelerated weathering.
Pulmonary COVID-19: Learning Spatiotemporal Features Combining CNN and LSTM Networks for Lung Ultrasound Video Classification

Deep Learning is a very active and important area for building Computer-Aided Diagnosis (CAD) applications. This work aims to present a hybrid model to classify lung ultrasound (LUS) videos captured by convex transducers to diagnose COVID-19. A Convolutional Neural Network (CNN) performed the extraction of spatial features, and the temporal dependence was learned using a Long Short-Term Memory (LSTM). Different types of convolutional architectures were used for feature extraction. The hybrid model (CNN-LSTM) hyperparameters were optimized using the Optuna framework. The best hybrid model was composed of an Xception pre-trained on ImageNet and an LSTM containing 512 units, configured with a dropout rate of 0.4, two fully connected layers containing 1024 neurons each, and a sequence of 20 frames in the input layer (20×2018). The model presented an average accuracy of 93% and sensitivity of 97% for COVID-19, outperforming models based purely on spatial approaches. Furthermore, feature extraction using transfer learning with models pre-trained on ImageNet provided comparable results to models pre-trained on LUS images. The results corroborate with other studies showing that this model for LUS classification can be an important tool in the fight against COVID-19 and other lung diseases.
Early Adverse Events between mRNA and Adenovirus-Vectored COVID-19 Vaccines in Healthcare Workers

Mass vaccination campaigns are important to control the COVID-19 pandemic, however, adverse events (AEs) contribute to vaccine hesitancy. To investigate and compare early AEs between the BNT162b2 mRNA and AZD1222 adenovirus-vectored vaccines, recipients completed daily surveys about local and systemic reactions for 7 days after each dose, respectively. A total of 80 and 1440 healthcare workers received two doses of BNT162b2 and a first dose of AZD1222 vaccines. Any AEs were reported by 52.5% of recipients after the first dose of BNT162b2, by 76.2% after the second dose of BNT162b2, and by 90.9% after the first dose of AZD1222 (p < 0.001). Younger vaccinees had more AEs after the second dose of BNT162b2 and first dose of AZD1222. Sex based differences were only observed in the AZD1222 recipient group. No incidence of anaphylaxis or neurologic AEs were observed. In conclusion, early AEs were mostly mild to moderate in severity and generally transient in both BNT162b2 and AZD1222 groups. Sufficient explanation of the expected AEs of the vaccine would be helpful for wider vaccination.
The role of deep learning‐based survival model in improving survival prediction of patients with glioblastoma

This retrospective study has been conducted to validate the performance of deep learning‐based survival models in glioblastoma (GBM) patients alongside the Cox proportional hazards model (CoxPH) and the random survival forest (RSF). Furthermore, the effect of hyperparameters optimization methods on improving the prediction accuracy of deep learning‐based survival models was investigated. Of the 305 cases, 260 GBM patients were included in our analysis based on the following criteria: demographic information (i.e., age, Karnofsky performance score, gender, and race), tumor characteristic (i.e., laterality and location), details of post‐surgical treatment (i.e., time to initiate concurrent chemoradiation therapy, standard treatment, and radiotherapy techniques), and last follow‐up time as well as the molecular markers (i.e., O‐6‐methylguanine methyltransferase and isocitrate dehydrogenase 1 status). Experimental results have demonstrated that age (Elderly > 65: hazard ratio [HR] = 1.63; 95% confidence interval [CI]: 1.213–2.18; p value = 0.001) and tumors located at multiple lobes ([HR] = 1.75; 95% [CI]: 1.177–2.61; p value = 0.006) were associated with poorer prognosis. In contrast, age (young < 40: [HR] = 0.57; 95% [CI]: 0.343–0.96; p value = 0.034) and type of radiotherapy (others include stereotactic and brachytherapy: [HR] = 0.5; 95%[CI]: 0.266–0.95; p value = 0.035) were significantly related to better prognosis. Furthermore, the proposed deep learning‐based survival model (concordance index [c‐index] = 0.823 configured by Bayesian hyperparameter optimization), outperformed the RSF (c‐index = 0.728), and the CoxPH model (c‐index = 0.713) in the training dataset. Our results show the ability of deep learning in learning a complex association of risk factors. Moreover, the remarkable performance of the deep‐learning‐based survival model could be promising to support decision‐making systems in personalized medicine for patients with GBM.
Characterization and Application of a Lytic Phage D10 against Multidrug-Resistant Salmonella

Salmonella is a widely distributed foodborne pathogen that is a serious threat to human health. The accelerated development of drug resistance and the increased demand for natural foods invoke new biocontrol agents to limit contamination by multidrug-resistant (MDR) Salmonella strains. In this study, a lytic Salmonella phage named D10 was characterized at the biological and genomic levels. D10 possesses a short latent period (10 min) and a large burst size (163 PFU/cell), as well as adequate stability under a range of pH conditions and moderate thermal tolerance. D10 effectively lysed different MDR Salmonella serovars and repressed their dynamic growth in the medium. Genomic analysis disclosed that D10 is a new member of the Siphoviridae family and lacks the genes implicated in lysogeny, pathogenicity, or antibiotic resistance. A three-ingredient phage cocktail was then developed by mixing D10 with previously identified myovirus D1-2 and podovirus Pu20. The cocktail significantly reduced the count of MDR strains in liquid eggs, regardless of the temperature applied (4 and 25 °C). These results suggest that phage D10 is a promising tool to prevent food contamination by MDR Salmonella.
Bioengineered Nanoparticles Loaded-Hydrogels to Target TNF Alpha in Inflammatory Diseases

Rheumatoid Arthritis (RA) is an incurable autoimmune disease that promotes the chronic impairment of patients’ mobility. For this reason, it is vital to develop therapies that target early inflammatory symptoms and act before permanent articular damage. The present study offers two novel therapies based in advanced drug delivery systems for RA treatment: encapsulated chondroitin sulfate modified poly(amidoamine) dendrimer nanoparticles (NPs) covalently bonded to monoclonal anti-TNF α antibody in both Tyramine-Gellan Gum and Tyramine-Gellan Gum/Silk Fibroin hydrogels. Using pro-inflammatory THP-1 (i.e., human monocytic cell line), the therapy was tested in an inflammation in vitro model under both static and dynamic conditions. Firstly, we demonstrated effective NP-antibody functionalization and TNF-α capture. Upon encapsulation, the NPs were released steadily over 21 days. Moreover, in static conditions, the approaches presented good anti-inflammatory activity over time, enabling the retainment of a high percentage of TNF α. To mimic the physiological conditions of the human body, the hydrogels were evaluated in a dual-chamber bioreactor. Dynamic in vitro studies showed absent cytotoxicity in THP-1 cells and a significant reduction of TNF-α in suspension over 14 days for both hydrogels. Thus, the developed approach showed potential for use as personalized medicine to obtain better therapeutic outcomes and decreased adverse effects.