bluuebunny/crossref_metadata_2025 huggingface dataset:
Market size asymmetry and strategic environmental policy in a Cournot model
Authors: Vidal-Meliá Lidia , Camacho-Cuena Eva , Ginés-Vilar Miguel
Published: March 2023

The achievement of Sustainable Development Goals (SDG) related to the environment requires identifying new sources of environmental degradation. This paper analyzes how market size asymmetry affects government decisions on environmental policy in the context of bilateral international trade and imperfect competition. We model an international duopoly with market size asymmetry and product heterogeneity. Each firm produces two different products, one for the domestic and one for the foreign markets, where the firms' production generates local emissions. When planning policies, the government in each country must choose between two options: an emission tax or a production subsidy. The findings of our paper underline the crucial role of market size asymmetry in determining the non-cooperative equilibrium policy in a setting where both firms and governments act strategically. We find that an increase in market size asymmetry between countries encourages governments to shift from emission taxes to production subsidies. Therefore, the environmental policy must consider these aspects to achieve greater effectiveness of regulation in favor of the environment. Actions to mitigate increased pollution should regulate production subsidies and improve the practices of governments and companies.
Towards automated video-based assessment of dystonia in dyskinetic cerebral palsy: A novel approach using markerless motion tracking and machine learning
Authors: Haberfehlner Helga , van de Ven Shankara S. , van der Burg Sven A. , Huber Florian , Georgievska Sonja , Aleo Ignazio , Harlaar Jaap , Bonouvrié Laura A. , van der Krogt Marjolein M. , Buizer Annemieke I.
Published: March 2023

Introduction: Video-based clinical rating plays an important role in assessing dystonia and monitoring the effect of treatment in dyskinetic cerebral palsy (CP). However, evaluation by clinicians is time-consuming, and the quality of rating is dependent on experience. The aim of the current study is to provide a proof-of-concept for a machine learning approach to automatically assess scoring of dystonia using 2D stick figures extracted from videos. Model performance was compared to human performance.

Methods: A total of 187 video sequences of 34 individuals with dyskinetic CP (8–23 years, all non-ambulatory) were filmed at rest during lying and supported sitting. Videos were scored by three raters according to the Dyskinesia Impairment Scale (DIS) for arm and leg dystonia (normalized scores ranging from 0–1). Coordinates in pixels of the left and right wrist, elbow, shoulder, hip, knee and ankle were extracted using DeepLabCut, an open source toolbox that builds on a pose estimation algorithm. Within a subset, tracking accuracy was assessed for a pretrained human model and for models trained with an increasing number of manually labeled frames. The mean absolute error (MAE) between DeepLabCut’s prediction of the position of body points and manual labels was calculated. Subsequently, movement and position features were calculated from extracted body point coordinates. These features were fed into a Random Forest Regressor to train a model to predict the clinical scores. The model performance trained with data from one rater evaluated by MAEs (model-rater) was compared to inter-rater accuracy.

Results: A tracking accuracy of 4.5 pixels (approximately 1.5 cm) could be achieved by adding 15–20 manually labeled frames per video. The MAEs for the trained models ranged from 0.21 ± 0.15 for arm dystonia to 0.14 ± 0.10 for leg dystonia (normalized DIS scores). The inter-rater MAEs were 0.21 ± 0.22 and 0.16 ± 0.20, respectively.

Conclusion: This proof-of-concept study shows the potential of using stick figures extracted from common videos in a machine learning approach to automatically assess dystonia. Sufficient tracking accuracy can be reached by manually adding labels within 15–20 frames per video. With a relatively small data set, it is possible to train a model that can automatically assess dystonia with a performance comparable to human scoring.
Using the electrodermal activity signal and machine learning for diagnosing sleep
Authors: Piccini Jacopo , August Elias , Óskarsdóttir María , Arnardóttir Erna Sif
Published: March 2023

Introduction

The use of the electrodermal activity (EDA) signal for health diagnostics is becoming increasingly popular. The increase is due to advances in computational methods such as machine learning (ML) and the availability of wearable devices capable of better measuring EDA signals. One field where work on EDA has significantly increased is sleep research, as changes in EDA are related to different aspects of sleep and sleep health such as sleep stages and sleep-disordered breathing; for example, obstructive sleep apnoea (OSA).

Methods

In this work, we used supervised machine learning, particularly the extreme gradient boosting (XGBoost) algorithm, to develop models for detecting sleep stages and OSA. We considered clinical knowledge of EDA during particular sleep stages and OSA occurrences, complementing a standard statistical feature set with EDA-specific variables.

Results

We obtained an average macro F1-score of 57.5% and 66.6%, depending on whether we considered five or four sleep stages, respectively. When detecting OSA, regardless of the severity, the model reached an accuracy of 83.7% or 78.4%, depending on the measure used to classify the participant's sleep health status.

Conclusion

The research work presented here provides further evidence that, in the future, most sleep health diagnostics might well do without complete polysomnography (PSG) studies, as wearables can detect well the EDA signal.
Sustainable coffee capsule consumption: Understanding Italian consumers' purchasing drivers
Authors: Samoggia Antonella , Busi Riccardo
Published: March 2023

Introduction

Coffee is among the most appreciated beverages by consumers globally. Single serve coffee market expands and is highly profitable, but coffee in capsules (CIC) raises a major issue of environmental sustainability. The coffee industry aims to expand the CIC sales and to reduce capsule environmental impact and waste. To better define a sustainability-oriented strategy, the coffee industry needs to understand how to approach different socio-economic consumer groups. Thus, this study aims to explore consumers' perception and awareness of sustainable CIC, with specific attention on consumers' socio-economic characteristics.

Methods

The study interviewed a sample of 261 Italian consumers. Data elaboration includes four main steps: a cluster analysis leading to three socio-economic groups, a factor and structural equation modeling to confirm the factors and the relation between CIC sustainability and quality factors, and a multinomial logistic regression to examine the factors that drive the likeliness of consumers' willingness to purchase sustainable CIC.

Results

Results support that all groups of consumers value sustainability attribute as main CIC purchasing decision driver. The sustainability factor does not show any significance on the explanation of the CIC quality factor. Consumer perception of the CIC sustainability is not affected by age, income, level of education, and gender.

Conclusion

This study provides preliminary insights for sustainable CIC production and consumption.
The influences of the advancement of green technology on agricultural CO2 release reduction: A case of Chinese agricultural industry
Authors: Xinxing Shi , Sarkar Apurbo , Yue Deng , Hongbin Zhu , Fangyuan Tian
Published: March 2023

The development of green technology (GT) may have a vital influence in decreasing carbon releases, and the linkage between the advancement of GT and CO2 releases in China's agricultural industry has not attracted enough attention. The main objectives of this study are to assess the influence of agricultural green technology advancement on efficiency enhancement, release control capabilities, agricultural energy structure, and agriculture industrial structure. This article decomposes the advancement of green technology (AGTP) in the agricultural industry in China into resource-saving green technology advancement (AEGTP) and emission reduction green technology advancement (ACGTP). At the same time, to evaluate the intermediary impact of green technology advancement, a two-step econometric model and an intermediary impact model were utilized to evaluate the panel data of 30 provinces in China from 1998 to 2018. The role of AGTP (including ACGTP and AEGTP) and CO2 release concentration has also been explored critically. The results show that (i) under the two-step measurement method, AGTP has substantial favorable impacts on agricultural energy efficiency (EF) and possesses a negative impact on agriculture industrial structure (PS) and agricultural energy structure (ES). Agricultural energy efficiency (EF) and agriculture industrial structure (PS) under AGTP will reduce CO2 release concentration, but the path of agricultural energy structure (ES) will increase CO2 release concentration. (ii) At the national level, AGTP has an immediate unfavorable influence on CO2 releases. After introducing the intermediary variables, the intermediary impact of AGTP on CO2 releases through agricultural energy efficiency (EF), agriculture industrial structure (PS), and agricultural energy structure (ES) is also significantly negative, and the direct impacts of each variable are higher than the intermediary impact. (iii) In terms of different zones, the direct impacts of AGTP are all significant. The order of significance of the direct impacts of different zones is west to central and central to eastern. The overall significance ranking of the mediating impact is ACGTP > AEGTP > AGTP, and the significance ranking of each index is ES > EF > PS. Finally, this article puts forward some policy recommendations to reduce CO2 releases.
Food values: How they relate to legality
Authors: Marchini Andrea , Polenzani Bianca , Ceccarelli Giulia , Mariano Eleonora , Martino Gaetano
Published: March 2023

Introduction

A value is an implicit principle that arises from judgments about everything around people, whereas a value system is a set of values ordered according to personal preferences. In this context, values can be seen as the adherence to an implicit or explicit set of rules, many of which are related to legality. In this study, legality is understood as the willingness of citizens to abide by the existing set of formal laws. A value system can guide consumers' food choices and provide information on their preferences. In this way, the citizens' food value system can influence the decisions of producers and policymakers.

Methods

The present study investigates the food value system of a sample of young adults using the Best-Worst Scale method.

Results

Values such as environmental impact rank high in their value system, indicating that adherence to a set of implicit rules and regulations concerning the environment are very important values when choosing food.

Discussion

Although adherence to a legal system that protects a public good (the environment) is considered an essential value, legality per se is not. This might suggest, on the one hand, a lack of awareness of legality issues in the Italian food system and, on the other hand, a strong interest in issues perceived as more urgent.
RETRACTED: Epitopes screening and vaccine molecular design of SADS-CoV based on immunoinformatics
Authors: Li Shinian , Chen Yaping
Published: March 2023

The regional outbreak of the Swine acute diarrhea syndrome coronavirus (SADS-CoV) has seriously threatened the swine industry. There is an urgent need to discover safe and effective vaccines to contain them quickly. The coronavirus spike protein mediates virus entry into host cells, one of the most important antigenic determinants and a potential vaccine target. Therefore, this study aims to conduct a predictive analysis of the epitope of S protein B cells and T cells (MHC class I and class II) by immunoinformatics methods by screening and identifying protective antigenic epitopes that induce major neutralized antibodies and activate immune responses to construct epitope vaccines. The study explored primary, secondary, and tertiary structures, disulfide bonds, protein docking, immune response simulation, and seamless cloning of epitope vaccines. The results show that the spike protein dominant epitope of the screening has a high conservativeness and coverage of IFN-γ, IL-4-positive Th epitope, and CTL epitope. The constructed epitope vaccine interacts stably with TLR-3 receptors, and the immune response simulation shows good immunogenicity, which could effectively activate humoral and cellular immunity. After codon optimization, it was highly likely to be efficiently and stably expressed in the Escherichia coli K12 expression system. Therefore, the constructed epitope vaccine will provide a new theoretical basis for the design of SADS-CoV antiviral drugs and related research on coronaviruses such as SARS-CoV-2.
ATG13 is involved in immune response of pathogen invasion in blood clam Tegillarca granosa
Authors: Wu Yuling , Liu Guosheng , Li Zengpeng , Chen Mingliang , Wang Qin
Published: March 2023

Mammalian autophagy-related gene 13 (ATG13) is a vital component of the ATG1 autophagy initiation complex which plays an essential role in autophagy. However, the molecular function of ATG13 in pathogen defense in invertebrates is still poorly understood. In this study, the full-length cDNA sequence of blood clam Tegillarca granosa ATG13 (TgATG13) was obtained, which was 1,918 bp in length, including 283 bp 5' UTR, 252 bp 3' UTR and 1,383 bp open reading frame (ORF) encoding 460 amino acids. Phylogenetic analysis revealed that TgATG13 had the closest relationship with that of Crassostrea Virginica. Quantitative real-time PCR results showed that the transcript of TgATG13 was universally expressed in various tissues of blood clam, with the highest expression level in hemocytes. The expression level of TgATG13 was robustly increased after exposure of both Vibrio alginolyticus and LPS. Fluorescence confocal microscopy further showed that TgATG13 promoted the production of autophagosome. In summary, our study demonstrated that TgATG13 was involved in the immune regulation of blood clam during pathogen invasion, deepening our understanding of the innate immune mechanism of blood clam.
Zoning of Potential Areas for the Production of Oleaginous Species in Colombia under Agroforestry Systems
Authors: Lozano-Castellanos Luisa F. , Méndez-Vanegas José E. , Tomatis Francisco , Correa-Guimaraes Adriana , Navas-Gracia Luis Manuel
Published: March 2023

Due to the need to develop more agroforestry systems, the Moringa oleifera, Olea Europea, Glycine max, Brassica napus, Helianthus annuus, and Jatropha curcas are identified as unconventional species for their expansion under these systems in Colombia. With the Colombian Environmental Information System (SIAC) database, zoning was carried out according to the agroclimatic species requirements and optimal coverage for their production. As a result, a total area of 212,977.2 km2 was identified, mainly including the departments of Casanare, Arauca, Vichada, Guajira, Córdoba, Meta, Magdalena, Cesar, Tolima, and Cundinamarca. The species and associations species with the most options for productive expansion are Moringa (75,758 km2), Moringa, Jatropha, and Sunflower (42,515.1 km2), Moringa and Jatropha (37,180.4 km2), Jatropha (20,840 km2), Jatropha and Sunflower (17,692.1 km2), Olive (7332.1 km2), and Soybean (3586.3 km2). Of the potential agroforestry areas to their establishment, 36% correspond to herbaceous and/or shrubby vegetation, 34% to grasses, and 22% to heterogeneous agricultural areas. This research is the first step to representing the agronomic versatility of these promising species and their potential contribution to the diversification of the agri-food and agroforestry sectors.
Composite Analysis of Competitiveness: Case Study of Companies Working the Soil in the Slovak Republic
Authors: Kravčáková Vozárová Ivana , Vavrek Roman , Adamišin Peter , Kotulič Rastislav
Published: March 2023

Competitiveness and sustainability in view of the constantly changing market environment are among the basic characteristics of a company, while its progress and growth are largely determined by the field of business and the nature of production. The introduction of the Common Agricultural Policy of the EU after the accession of the Slovak Republic to the EU significantly changed the necessary conditions for the functioning of domestic agriculture. Agriculture in the European Union is highly differentiated; therefore, surveys on the sustainability and competitiveness of agriculture at the national level are very valuable. Agricultural cooperatives in Slovakia compete for market share with non-cooperative organizations. In our research, we used a sample of enterprises in the agricultural sector, and, using the RCR coefficient, we evaluated the competitiveness of enterprises from the point of view of individual legal forms. The purpose of our research is to answer a simple question: What is the cause of the recorded development of the RCR index? In the case of non-cooperatives, the value of the regressor is higher in each comparison with the results of cooperatives. However, the nature of the relationship in the case of entities operating on agricultural land in the Slovak Republic with the legal form of a cooperative is significantly more homogeneous across the evaluated period.
Experience in the Cultivation of a New Perennial Cereal Crop—Trititrigia in the Conditions of South of the Rostov Region
Authors: Lachuga Yuri , Meskhi Besarion , Pakhomov Viktor , Semenikhina Yulia , Kambulov Sergey , Rudoy Dmitry , Maltseva Tatyana
Published: March 2023

The article presents the research results on the cultivation of a new perennial cereal crop of winter wheat hybrid and wheatgrass—perennial winter wheat (Trititrigia) of the “Pamyati Lyubimovoy” variety (hereinafter—Trititrigia) in the southern zone of the Rostov region over two years. The purpose of the research is to assess the degree of suitability for the use of a new perennial cereal crop—Trititrigia in the southern zone of the Rostov region. The yield, technological indicators of grain, and baking properties of flour were taken as evaluation criteria. The study of all aspects of Trititrigia cultivation was carried out in comparative sowings with winter wheat of the Stanichnaya variety, common in the Rostov region, of the Agricultural Research Center “Donskoy” (ARC Donskoy). As a result of the research, it was found that the average biological yield of Trititrigia in the southern zone of the Rostov region in two years was 4.28 t ha−1, which was 1.57 t ha−1 less than that of the control sowing of winter wheat of the Stanichnaya variety. The weight of the straw part with an ear of Trititrigia is 1.9 times higher than that of winter wheat of the “Stanichnaya” variety. Technological indicators of the quality of Trititrigia grain corresponded to the first class in terms of amount of protein (more than 19%), gluten (33.34%), and falling number (274 s); the third class according to the gluten deformation index (GDI) (81.5 points); the fifth class according to the natural mass of grain (691 g L−1). The general baking evaluation of Trititrigia grain allowed it to be classified as valuable wheat.
Efficacy of Nitrogen against Stored Product Insects with Different Susceptibility Levels to Phosphine in Industrial Applications
Authors: Sakka Maria K. , Athanassiou Christos G.
Published: March 2023

We carried out trials on the commercial applications of nitrogen in different industrial structures, using phosphine-susceptible and -resistant populations of three stored product beetle species, the red flour beetle, Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae), the rice weevil, Sitophilus oryzae (L.) (Coleoptera: Curculionidae), and the saw-toothed grain beetle, Oryzaephilus surinamensis (L.) (Coleoptera: Silvanidae). Twelve different trials were conducted in total, five in chambers and seven in large silos in Greece, following different temperatures and exposure times. In most of our trials, complete mortality was recorded, with the exception of two silos in which survival was recorded for T. castaneum and O. surinamensis, while S. oryzae was classified as the most susceptible species. Moreover, low or no progeny production was recorded for most of the trials. Our results indicate that nitrogen, through the reduction in the oxygen level, could be used for the control of stored product insects that are resistant to phosphine, and can be further utilized in resistance management strategies.
Cation Exchange Properties of Subsurface Soil in Mid-Subtropical China: Variations, Correlation with Soil-Forming Factors, and Prediction
Authors: Ouyang Ningxiang , Zhang Pengbo , Zhang Yangzhu , Sheng Hao , Zhou Qing , Huang Yunxiang , Yu Zhan
Published: March 2023

Soil cation exchange property (SCEP) is important in soil development and environmental buffering. However, the variations in SCEP and its correlation with soil-forming factors in subsurface soil are not fully understood. In this study, we quantified the surface and subsurface SCEP variations as a function of parent material, vegetation, hillslope position, and soil type. Fifty upland soil profiles from mid-subtropical China were selected. The cation exchange capacity (CEC) and effective CEC (ECEC) of subsurface soil were significantly higher in soils derived from slate, Quaternary red clay (QRC), and limestone than in soils derived from granite and sandstone. The subsurface soils derived from limestone had the highest base saturation (BS) and the sum of base cations, and the lowest aluminum (Al) saturation. The SCEP in surface soil significantly varied with vegetation and hillslope position. The surface soil CEC was the highest in mixed-forest vegetation, whereas the ECEC and exchangeable acidity (EA) were the highest in arable vegetation. Exchangeable potassium (K+) was lowest and the EA was highest in soil orders at the strong development phase. Exchangeable calcium (Ca2+), magnesium (Mg2+), CEC, and BS were the highest in soil orders at the intermediate development phase. The prediction accuracy of soil CEC using the random forest model was higher than that obtained using multiple stepwise regression, with the best results (R2 = 0.92) obtained in the surface soil. Our study indicated that the SCEP in surface and subsurface soils was controlled by different soil-forming factors and could be effectively predicted by soil properties in subtropical China.
Plant Architecture Influences the Population Transpiration and Canopy Temperature in Winter Wheat Genotypes
Authors: Huang Guirong , Zhang Xinying , Wang Zhenzhao , Li Yuzhong , Liu Xiaoying , Guo Rui , Gu Fengxue , Liu Enke , Li Shuying , Zhong Xiuli , Mei Xurong
Published: March 2023

To study how plant architecture affects the canopy traits and water use of wheat, the Triticum aestivum L. population is expected to provide important information for cultivar improvement and the ideal population structure establishment for conserving water without causing an enormous grain yield loss. This study was conducted for three consecutive growing seasons using two genotypes with contrasting plant architectures as the materials, the upright-leafed compact type Jing 411 and the flat-leafed loose type Jinmai 47. The population-scale transpiration (PT) and soil evaporation (E) were partitioned from the evapotranspiration (ET) using micro-lysimeters, and the canopy traits population density and the canopy temperature depression (CTD) were also monitored during the period from the jointing to early grain filling stage—the largest water requirement period of wheat crops. Jinmai 47 showed a lower E than Jing 411, but a similar PT and ET, though it had a higher population density at the sowing density. The total evapotranspiration (TET) for the whole growing season was also similar in the two genotypes. This indicated that Jinmai 47 performed better in water conservation than Jing 411. With a similar PT and TET, however, Jinmai 47 showed a rather larger CTD and a significantly higher grain yield than Jing 411. If the higher population density and higher leaf net photosynthetic rate could explain its higher grain yield, the higher leaf stomotal conductance and transpiration rate and the higher population density could not explain the similar PT, ET and TET to Jing 411. Presumably, the involvement of the plant architecture disrupted the original higher transpiration–larger CTD relation, and broke up the prevailing saving water-losing yield concept. Thus, the study might suggest the important water saving value of the flat-leafed loose architecture in wheat crops and demonstrate the possibility of conserving irrigation water without causing serious grain yield loss by taking advantage of the distinct plant architecture to establish an appropriate population structure.
Design of an Educational Chatbot Using Artificial Intelligence in Radiotherapy
Authors: Chow James C. L. , Sanders Leslie , Li Kay
Published: March 2023

Context: In cancer centres and hospitals particularly during the pandemic, there was a great demand for information, which could hardly be handled by the limited manpower available. This necessitated the development of an educational chatbot to disseminate topics in radiotherapy customized for various user groups, such as patients and their families, the general public and radiation staff. Objective: In response to the clinical demands, the objective of this work is to explore how to design a chatbot for educational purposes in radiotherapy using artificial intelligence. Methods: The chatbot is designed using the dialogue tree and layered structure, incorporated with artificial intelligence features such as natural language processing (NLP). This chatbot can be created in most platforms such as the IBM Watson Assistant and deposited in a website or various social media. Results: Based on the question-and-answer approach, the chatbot can provide humanlike communication to users requesting information on radiotherapy. At times, the user, often worried, may not be able to pinpoint the question exactly. Thus, the chatbot will be user friendly and reassuring, offering a list of questions for the user to select. The NLP system helps the chatbot to predict the intent of the user so as to provide the most accurate and precise response to him or her. It is found that the preferred educational features in a chatbot are functional features such as mathematical operations, which should be updated and modified routinely to provide new contents and features. Conclusions: It is concluded that an educational chatbot can be created using artificial intelligence to provide information transfer to users with different backgrounds in radiotherapy. In addition, testing and evaluating the performance of the chatbot is important, in response to user’s feedback to further upgrade and fine-tune the chatbot.
Sow Nutrition, Uterine Contractions, and Placental Blood Flow during the Peri-Partum Period and Short-Term Effects on Offspring: A Review
Authors: van den Bosch Moniek , Soede Nicoline , Kemp Bas , van den Brand Henry
Published: March 2023

The birth process is a crucial event for piglet survival. Along with increasing litter sizes, not only has the duration of parturition increased, but placental blood flow per piglet has reduced and placental area per piglet has become smaller, making these piglets more susceptible for hypoxia. Diminishing the risk of piglet hypoxia by either reducing the total duration of parturition or increasing fetal oxygenation may reduce the incidence of stillbirth and early post-partum mortality. This review discusses options to do so by nutritionally supporting the sow in the final pre-partum period, after discussing the role of uterine contractions and placental blood flow. Providing sufficient energy seems to be a logical first step, but also other nutrients needed for uterine contractions, such as calcium, or enhancing uterine blood flow by using nitrate seem promising. These nutrient requirements may depend on litter size.
Intravenous Immunoglobulins as Immunomodulators in Autoimmune Diseases and Reproductive Medicine
Authors: Velikova Tsvetelina , Sekulovski Metodija , Bogdanova Simona , Vasilev Georgi , Peshevska-Sekulovska Monika , Miteva Dimitrina , Georgiev Tsvetoslav
Published: March 2023

Intravenous administration of immunoglobulins has been routinely used for more than 60 years in clinical practice, developed initially as replacement therapy in immunodeficiency disorders. Today, the use of intravenous immunoglobulins (IVIGs) is embedded in the modern algorithms for the management of a few diseases, while in most cases, their application is off-label and thus different from their registered therapeutic indications according to the summary of product characteristics. In this review, we present the state-of-the-art use of IVIGs in various autoimmune conditions and immune-mediated disorders associated with reproductive failure, as approved therapy, based on indications or off-label. IVIGs are often an alternative to other treatments, and the administration of IVIGs continues to expand as data accumulate. Additionally, new insights into the pathophysiology of immune-mediated disorders have been gained. Therefore, the need for immunomodulation has increased, where IVIG therapy represents an option for stimulating, inhibiting and regulating various immune processes.
Gut Microbiota Composition Can Predict Colonization by Multidrug-Resistant Bacteria in SARS-CoV-2 Patients in Intensive Care Unit: A Pilot Study
Authors: García-García Jorge , Diez-Echave Patricia , Yuste María Eugenia , Chueca Natalia , García Federico , Cabeza-Barrera Jose , Fernández-Varón Emilio , Gálvez Julio , Colmenero Manuel , Rodríguez-Cabezas Maria Elena , Rodríguez-Nogales Alba , Morón Rocío
Published: March 2023

The SARS-CoV-2 infection has increased the number of patients entering Intensive Care Unit (ICU) facilities and antibiotic treatments. Concurrently, the multi-drug resistant bacteria (MDRB) colonization index has risen. Considering that most of these bacteria are derived from gut microbiota, the study of its composition is essential. Additionally, SARS-CoV-2 infection may promote gut dysbiosis, suggesting an effect on microbiota composition. This pilot study aims to determine bacteria biomarkers to predict MDRB colonization risk in SARS-CoV-2 patients in ICUs. Seventeen adult patients with an ICU stay >48 h and who tested positive for SARS-CoV-2 infection were enrolled in this study. Patients were assigned to two groups according to routine MDRB colonization surveillance: non-colonized and colonized. Stool samples were collected when entering ICUs, and microbiota composition was determined through Next Generation Sequencing techniques. Gut microbiota from colonized patients presented significantly lower bacterial diversity compared with non-colonized patients (p < 0.05). Microbiota in colonized subjects showed higher abundance of Anaerococcus, Dialister and Peptoniphilus, while higher levels of Enterococcus, Ochrobactrum and Staphylococcus were found in non-colonized ones. Moreover, LEfSe analysis suggests an initial detection of Dialister propionicifaciens as a biomarker of MDRB colonization risk. This pilot study shows that gut microbiota profile can become a predictor biomarker for MDRB colonization in SARS-CoV-2 patients.
Spiramycin Disarms Pseudomonas aeruginosa without Inhibiting Growth
Authors: Calcagnile Matteo , Jeguirim Inès , Tredici Salvatore Maurizio , Damiano Fabrizio , Alifano Pietro
Published: March 2023

Spiramycin is a 16-membered macrolide antibiotic currently used in therapy to treat infections caused by Gram-positive bacteria responsible for respiratory tract infections, and it is also effective against some Gram-negative bacteria and against Toxoplasma spp. In contrast, Pseudomonas aeruginosa, which is one of the pathogens of most concern globally, is intrinsically resistant to spiramycin. In this study we show that spiramycin inhibits the expression of virulence determinants in P. aeruginosa in the absence of any significant effect on bacterial multiplication. In vitro experiments demonstrated that production of pyoverdine and pyocyanin by an environmental strain of P. aeruginosa was markedly reduced in the presence of spiramycin, as were biofilm formation, swarming motility, and rhamnolipid production. Moreover, treatment of P. aeruginosa with spiramycin sensitized the bacterium to H2O2 exposure. The ability of spiramycin to dampen the virulence of the P. aeruginosa strain was confirmed in a Galleria mellonella animal model. The results demonstrated that when G. mellonella larvae were infected with P. aeruginosa, the mortality after 24 h was >90%. In contrast, when the spiramycin was injected together with the bacterium, the mortality dropped to about 50%. Furthermore, marked reduction in transcript levels of the antimicrobial peptides gallerimycin, gloverin and moricin, and lysozyme was found in G. mellonella larvae infected with P. aeruginosa and treated with spiramycin, compared to the larvae infected without spiramycin treatment suggesting an immunomodulatory activity of spiramycin. These results lay the foundation for clinical studies to investigate the possibility of using the spiramycin as an anti-virulence and anti-inflammatory drug for a more effective treatment of P. aeruginosa infections, in combination with other antibiotics.
Enhanced Levels of Peroxisome-Derived H2O2 Do Not Induce Pexophagy but Impair Autophagic Flux in HEK-293 and HeLa Cells
Authors: Li Hongli , Lismont Celien , Costa Cláudio F. , Hussein Mohamed A. F. , Baes Myriam , Fransen Marc
Published: March 2023

Peroxisomes are functionally specialized organelles that harbor multiple hydrogen peroxide (H2O2)-producing and -degrading enzymes. Given that this oxidant functions as a major redox signaling agent, peroxisomes have the intrinsic ability to mediate and modulate H2O2-driven processes, including autophagy. However, it remains unclear whether changes in peroxisomal H2O2 (po-H2O2) emission impact the autophagic process and to which extent peroxisomes with a disturbed H2O2 metabolism are selectively eliminated through a process called “pexophagy”. To address these issues, we generated and validated HEK-293 and HeLa pexophagy reporter cell lines in which the production of po-H2O2 can be modulated. We demonstrate that (i) po-H2O2 can oxidatively modify multiple selective autophagy receptors and core autophagy proteins, (ii) neither modest nor robust levels of po-H2O2 emission act as a prime determinant of pexophagy, and (iii) high levels of po-H2O2 impair autophagic flux by oxidative inhibition of enzymes involved in LC3II formation. Unexpectedly, our analyses also revealed that the autophagy receptor optineurin can be recruited to peroxisomes, thereby triggering pexophagy. In summary, these findings lend support to the idea that, during cellular and organismal aging, peroxisomes with enhanced H2O2 release can escape pexophagy and downregulate autophagic activity, thereby perpetuating the accumulation of damaged and toxic cellular debris.
Deep Neural Network Modeling for CFD Simulations: Benchmarking the Fourier Neural Operator on the Lid-Driven Cavity Case
Authors: Costa Rocha Paulo Alexandre , Johnston Samuel Joseph , Oliveira Santos Victor , Aliabadi Amir A. , Thé Jesse Van Griensven , Gharabaghi Bahram
Published: March 2023

In this work we present the development, testing and comparison of three different physics-informed deep learning paradigms, namely the ConvLSTM, CNN-LSTM and a novel Fourier Neural Operator (FNO), for solving the partial differential equations of the RANS turbulence model. The 2D lid-driven cavity flow was chosen as our system of interest, and a dataset was generated using OpenFOAM. For this task, the models underwent hyperparameter optimization, prior to testing the effects of embedding physical information on performance. We used the mass conservation of the model solution, embedded as a term in our loss penalty, as our physical information. This approach has been shown to give physical coherence to the model results. Based on the performance, the ConvLSTM and FNO models were assessed in forecasting the flow for various combinations of input and output timestep sizes. The FNO model trained to forecast one timestep from one input timestep performed the best, with an RMSE for the overall x and y velocity components of 0.0060743 m·s−1.
ColorMedGAN: A Semantic Colorization Framework for Medical Images
Authors: Chen Shaobo , Xiao Ning , Shi Xinlai , Yang Yuer , Tan Huaning , Tian Jiajuan , Quan Yujuan
Published: March 2023

Colorization for medical images helps make medical visualizations more engaging, provides better visualization in 3D reconstruction, acts as an image enhancement technique for tasks such as segmentation, and makes it easier for non-specialists to perceive tissue changes and texture details in medical images in diagnosis and teaching. However, colorization algorithms have been hindered by limited semantic understanding. In addition, current colorization methods still rely on paired data, which is often not available for specific fields such as medical imaging. To address the texture detail of medical images and the scarcity of paired data, we propose a self-supervised colorization framework based on CycleGAN(Cycle-Consistent Generative Adversarial Networks), treating the colorization problem of medical images as a cross-modal domain transfer problem in color space. The proposed framework focuses on global edge features and semantic information by introducing edge-aware detectors, multi-modal discriminators, and a semantic feature fusion module. Experimental results demonstrate that our method can generate high-quality color medical images.
Dimension Reduction and Redundancy Removal through Successive Schmidt Decompositions
Authors: Daskin Ammar , Gupta Rishabh , Kais Sabre
Published: March 2023

Quantum computers are believed to have the ability to process huge data sizes, which can be seen in machine learning applications. In these applications, the data, in general, are classical. Therefore, to process them on a quantum computer, there is a need for efficient methods that can be used to map classical data on quantum states in a concise manner. On the other hand, to verify the results of quantum computers and study quantum algorithms, we need to be able to approximate quantum operations into forms that are easier to simulate on classical computers with some errors. Motivated by these needs, in this paper, we study the approximation of matrices and vectors by using their tensor products obtained through successive Schmidt decompositions. We show that data with distributions such as uniform, Poisson, exponential, or similar to these distributions can be approximated by using only a few terms, which can be easily mapped onto quantum circuits. The examples include random data with different distributions, the Gram matrices of iris flower, handwritten digits, 20newsgroup, and labeled faces in the wild. Similarly, some quantum operations, such as quantum Fourier transform and variational quantum circuits with a small depth, may also be approximated with a few terms that are easier to simulate on classical computers. Furthermore, we show how the method can be used to simplify quantum Hamiltonians: In particular, we show the application to randomly generated transverse field Ising model Hamiltonians. The reduced Hamiltonians can be mapped into quantum circuits easily and, therefore, can be simulated more efficiently.
A Quantum-Based Beetle Swarm Optimization Algorithm for Numerical Optimization
Authors: Yu Lin , Ren Jieqi , Zhang Jie
Published: March 2023

The beetle antennae search (BAS) algorithm is an outstanding representative of swarm intelligence algorithms. However, the BAS algorithm still suffers from the deficiency of not being able to handle high-dimensional variables. A quantum-based beetle swarm optimization algorithm (QBSO) is proposed herein to address this deficiency. In order to maintain population diversity and improve the avoidance of falling into local optimal solutions, a novel quantum representation-based position updating strategy is designed. The current best solution is regarded as a linear superposition of two probabilistic states: positive and deceptive. An increase in or reset of the probability of the positive state is performed through a quantum rotation gate to maintain the local and global search ability. Finally, a variable search step strategy is adopted to speed up the ability of the convergence. The QBSO algorithm is verified against several swarm intelligence optimization algorithms, and the results show that the QBSO algorithm still has satisfactory performance at a very small population size.
A Semi-Supervised Learning Framework for Machining Feature Recognition on Small Labeled Sample
Authors: Wu Hongjin , Lei Ruoshan , Huang Pei , Peng Yibing
Published: March 2023

Automated machining feature recognition is an essential component linking computer-aided design (CAD) and computer-aided process planning (CAPP). Deep learning (DL) has recently emerged as a promising method to improve machining feature recognition. However, training DL-based recognition models typically require annotating large amounts of data, which is time-consuming and labor-intensive for researchers. Additionally, DL models struggle to achieve satisfactory results when presented with small labeled datasets. Furthermore, existing DL-based approaches require significant memory and processing time, thus hindering their real-world application. To address these challenges, this paper presents a semi-supervised learning framework that leverages both labeled and unlabeled data to learn meaningful visual representations. Specifically, self-supervised learning is utilized to extract prior knowledge from a large dataset without annotations, which is then transferred to improve downstream feature recognition tasks. Furthermore, we apply lightweight network techniques to two established feature recognizers, FeatureNet and MsvNet, to develop reduced-memory, computationally efficient models termed FeatureNetLite and MsvNetLite, respectively. To validate the effectiveness of the proposed approaches, we conducted comparative studies on the FeatureNet dataset. With only one training sample per class, MsvNetLite outperformed MsvNet by about 19%, whereas FeatureNetLite outperformed FeatureNet by approximately 20% in machining feature classification. On a common X86 CPU, MsvNetLite gained 6.68× improvement in speed over MsvNet, and FeatureNetLite was 2.49× faster than FeatureNet. The proposed semi-supervised learning framework shows a significant improvement in machining feature recognition on small labeled data while achieving the optimal balance between recognition accuracy and inference speed compared to other DL-based approaches.
Facies and Origin of Tufa Deposits from the Gostilje River Basin and the Sopotnica River Basin (SW Serbia)
Authors: Batoćanin Natalija , Wróblewski Wojciech , Carević Ivana , Durlević Uroš , Gajić Violeta , Valjarević Aleksandar
Published: March 2023

Tufa accumulations from the Gostilje River Basin and the Sopotnica River Basin in SW Serbia are represented by both active and fossil tufa precipitates. The aim of this study is to distinguish and describe different tufa facies and to determine the environmental conditions, based on stable isotope data. We also compare our analysis with other tufa deposits in Europe. Four facies are distinguished: moss tufa, algal tufa, stromatolitic laminated tufa, and phytoclastic tufa. The dominant constituent of all tufa samples is low Mg-calcite, whereas the presence of sylvite is noted in two samples from the Gostilje River Basin. The δ18O values range from −9.07‰ to −10.79‰ (mean value: −9.81‰), while the δ13C values range from −6.50‰ to −10.34‰ (mean values −9.01‰). The stable isotope values (δ13C and δ18O) indicate that these tufa deposits were precipitated from cold, ambient water supported by CO2 of an atmospheric origin. We emphasize that this is the first data about stable isotope analyses of tufa deposits from Serbia.
Could Mini-Trampoline Training Be Considered as a New Strategy to Reduce Asymmetries?
Authors: De Maio Marianna , Di Rocco Francesca , Papale Olga , Festino Emanuel , Fusco Andrea , Cortis Cristina
Published: March 2023

Inter-limb asymmetry is defined as the performance imbalance of a limb with respect to the other one. Studies demonstrated how plyometric training could minimize inter-limb imbalance by reducing asymmetries and the relative risk of musculoskeletal injuries. Among the different plyometric training modalities, SuperJump® represented a reliable method. This study aimed to evaluate the acute effects of SuperJump® training on dynamic balance Unilateral Asymmetry and Bilateral Asymmetry Indexes. Thirty-seven subjects were randomly allocated in two groups: SuperJump® (N = 20) and Control (N = 17). The SuperJump® group participated in the SuperJump® session, whereas the Control group did not receive any workout session. Before (PRE) and after (POST) the SuperJump® session, both groups performed the Wobble Board balance test. A significant difference (p < 0.003) between the SuperJump® in POST and Control groups in PRE intervention for dominant leg was found. No significant differences (p > 0.05) emerged between groups in testing time on Bilateral Asymmetry Index. Significant differences (p = 0.005) between PRE and POST in the SuperJump® group and an 18.9% Unilateral Asymmetry Index threshold reduction for the subjects were found. A strong relationship (R2 = 0.79) between delta (∆) change Unilateral Asymmetry Index and Unilateral Asymmetry Index baseline parameters was tested. Lastly, no significant differences (p > 0.05) in Unilateral Asymmetry Indexes between the Bilateral Asymmetry equal and change categories in the SuperJump® group was found. In conclusion, SuperJump® training played a key role in reducing Unilateral Asymmetry Index.
Development and Validation of an HPLC-UV Method for the Determination Bis(2-ethylhexyl) Phthalate Ester in Alcoholic Beverages
Authors: Gemenetzis Evangelos G. , Alygizakis Nikiforos A.
Published: March 2023

An HPLC method with UV detector was developed for the determination of DEHP phthalate ester in the alcoholic beverage “Ouzo”. Phthalate esters are added to plastic packaging for food and beverages to increase flexibility, transparency, strength, and longevity. When these substances come into contact with food or beverages, they can lead to the migration of phthalate residues into the product. This paper presents a two-step process involving extraction of the sample with hexane and separation of the phthalates by HPLC. The method was validated for specificity, linearity, limit of quantification, accuracy, precision, range, and ruggedness. The linear range is 0.3–1.5 mg/L DEHP, with a lower limit of quantification of 0.06 mg/L. The precision study showed acceptable RSD values, and the working range is 0.3 to 1.5 mg/L DEHP. The relative standard uncertainty of DEHP determination in Ouzo was ±8%. The results show that the in-house method is suitable, reliable, and fit-for-purpose.