bluuebunny/crossref_metadata_2025 huggingface dataset:
A deep learning latent variable model to identify children with autism through motor abnormalities
Authors: Milano Nicola , Simeoli Roberta , Rega Angelo , Marocco Davide
Published: May 2023

Introduction

Autism Spectrum Disorder (ASD) is a by-birth neurodevelopmental disorder difficult to diagnose owing to the lack of clinical objective and quantitative measures. Classical diagnostic processes are time-consuming and require many specialists’ collaborative efforts to be properly accomplished. Most recent research has been conducted on automated ASD detection using advanced technologies. The proposed model automates ASD detection and provides a new quantitative method to assess ASD.

Methods

The theoretical framework of our study assumes that motor abnormalities can be a potential hallmark of ASD, and Machine Learning may represent the method of choice to analyse them. In this study, a variational autoencoder, a particular type of Artificial Neural Network, is used to improve ASD detection by analysing the latent distribution description of motion features detected by a tablet-based psychometric scale.

Results

The proposed ASD detection model revealed that the motion features of children with autism consistently differ from those of children with typical development.

Discussion

Our results suggested that it could be possible to identify potential motion hallmarks typical for autism and support clinicians in their diagnostic process. Potentially, these measures could be used as additional indicators of disorder or suspected diagnosis.
Investigating suicidality across the autistic-catatonic continuum in a clinical sample of subjects with major depressive disorder and borderline personality disorder
Authors: Dell’Osso Liliana , Nardi Benedetta , Bonelli Chiara , Gravina Davide , Benedetti Francesca , Amatori Giulia , Battaglini Simone , Massimetti Gabriele , Luciano Mario , Berardelli Isabella , Brondino Natascia , De Gregorio Marianna , Deste Giacomo , Nola Marta , Reitano Antonino , Muscatello Maria Rosaria Anna , Pompili Maurizio , Politi Pierluigi , Vita Antonio , Carmassi Claudia , Cremone Ivan Mirko , Carpita Barbara , Maj Mario
Published: May 2023

Background

Recent literature has highlighted that catatonia may be more prevalent among psychiatric patients than previously thought, beginning from autism spectrum disorders (ASD), for which it has been suggested to represent a severe late consequence, but also among individuals with mood disorders and borderline personality disorder (BPD). Interestingly, one critical point shared by these conditions is the increased risk of suicidality. The aim of this study was to evaluate how the presence and the prevalence of catatonic symptoms may shape and correlate with suicidal risk in a sample of individuals with major depressive disorder (MDD) or BPD.

Methods

We recruited two clinical samples of subjects (BPD and MDD) and a control group without a diagnosis according to DSM-5 (CTL). Subjects were assessed with the catatonia spectrum (CS) and the MOODS-SR for evaluating suicidality.

Results

In the total sample, suicidality score was significantly and positively correlated with all CS domains and CS total score. Correlation and regression analyses highlighted specific patterns of association among Catatonia spectrum domains and suicidality in the MDD and BPD group and in the total sample.

Conclusion

In both disorders, higher catatonic traits are linked to higher suicidal tendencies, confirming the high risk of suicide for this population. However, different patterns of association between catatonic symptoms and suicidality were highlighted in the two disorders.
Correlation of systemic immune-inflammation index and moderate/major depression in patients with depressive disorders: a large sample cross-sectional study
Authors: Cui Shu , Li Juanjuan , Liu Yun , Yao Gaofeng , Wu Yanhai , Liu Zhiwei , Sun Liang , Sun Longlong , Liu Huanzhong
Published: May 2023

Objective

To evaluate the clinical value of systemic immune-inflammation index (SII) based on peripheral blood neutrophil, lymphocyte, and platelet count in evaluating the subtype and severity of depression in patients with depressive disorder.

Methods

This retrospective cohort study was conducted in the Third People’s Hospital of Fuyang City from January 1, 2020 to December 31, 2022. The data included sociodemographic information at admission, clinical data, discharge diagnosis and inflammatory markers. Patients were divided into low SII group and high SII group according to the optimal threshold of SII determined by receiver operating characteristic curve (ROC curve). Binary logistic regression was used to analyze the correlation between moderate/major depression and SII level.

Results

Compared to the low SII group, the high SII group had a higher age level (χ2 = 7.663, p = 0.006), more smokers (χ2 = 9.458, p = 0.002), more moderate/major depression patients (χ2 = 45.645, p < 0.001), and a higher proportion of patients with accompanying somatic symptoms (χ2 = 14.867, p < 0.001). In the final logistic regression model, after controlling for confounding factors, SII at admission was significantly associated with moderate/major depression [β =1.285, p < 0.001; odds ratio (95% confidence intervals) = 3.614 (2.693–4.850)]. Patients with high SII scores were 3.614 times more likely to have moderate/severe depression than those with low SII scores. We propose a cut-off value of SII =540.78 (sensitivity = 36.4% and specificity = 80.3%) according to the maximum Youden index.

Conclusion

Our research indicates that SII may be a useful, repeatable, convenient, and affordable index to identify moderate/major depression in depressive disorder.
Macro-structural predictors of Australian family day care quality
Authors: Char Vincent , Harrison Linda J. , Li Hui
Published: May 2023

Introduction

This study explores the predictive power of macro-structural characteristics on quality rating and improvement system (QRIS) outcomes of Family Day Care (FDC) services in Australia.

Methods

The dataset consisted of 441 FDC National Quality Standard (NQS) ratings from all Australian states and territories, with overall ratings of Exceeding NQS, Meeting NQS, Working Towards NQS, or Significant Improvement Required.

Results

Multinomial logistic regressions confirmed that management type, community socioeconomic status (SES), level of urbanization, and government jurisdiction explained 6.9 to 19.3% of the variation in QRIS outcomes. Results indicated that lower FDC NQS ratings were more likely for (1) private for-profit vs. not-for-profit; (2) low-SES vs. high-SES area; and (3) regional or remote area vs. metropolitan. State/territory jurisdiction also influenced NQS ratings.

Discussion

These findings imply the need for policy attention to inequalities in FDC quality associated with systemic and organizational differences. Greater effort is needed to promote equality and equity in FDC services.
Strengthening national public health institutes: a systematic review on institution building in the public sector
Authors: Brugnara Lucia , Jaramillo Catalina , Olarte-Peña Margarita , Karl Larissa , Deckert Andreas , Marx Michael , Horstick Olaf , Dambach Peter , Fehr Angela
Published: May 2023

Introduction

Strong and efficient institutions are vital to the development of well-functioning governments and strong societies. The term “institution building” encompasses the creation, support, development, and strengthening of organizations and institutions. Still, there is little aggregated evidence on “institution building” considering a wider system-thinking approach, best practices, or development cooperation specifically in the field of public health. In 2007, the International Association of National Public Health Institutes (IANPHI) created a guiding Framework that countries may use for developing National Public Health Institutes (NPHIs). This Framework is currently being revised.

Methods

In this context, we conducted a systematic review to facilitate this revision with recent evidence on institution building and its potential contribution to NPHI. We followed the PRISMA guidelines for systematic reviews, searching for relevant publications in seven scientific databases (Pubmed, VHL/LILACS, EconLit, Google Scholar, Web of Science, World Affairs Online, ECONBIZ) and four libraries (World Bank; European Health for All database of the World Health Organization European Region, WHO; Organization for Economic Cooperation and Development, OECD; and the African Union Common Repository). The search was carried out in October 2021. We used the “framework analysis” tool for systematically processing documents according to key themes.

Results

As a result, we identified 3,015 records, of which we included 62 documents in the final review. This systematic review fills a major gap of aggregated information on institution building in the field of public health and National Public Health Institutes. It is to our knowledge the first systematic review of this kind. The overriding result is the identification and definition of six domains of institution building in the health sector: “governance,” “knowledge and innovation,” “inter-institutional cooperation,” “monitoring and control,” “participation,” and “sustainability and context-specific adaptability.”

Discussion

Our results show that the described domains are highly relevant to the public health sector, and that managers and the scientific community recognize their importance. Still, they are often not applied consistently when creating or developing NPHIs. We conclude that organizations engaged in institution building of NPHIs, including IANPHI, may greatly benefit from state-of-the-art research on institution building as presented in this study.
The method behind the unprecedented production of indicators of the presence of languages in the Internet
Authors: Pimienta Daniel , Blanco Álvaro , de Oliveira Gilvan Müller
Published: May 2023

Reliable and updated indicators of the presence of languages in the Internet are required to drive efficiently policies for languages, to forecast e-commerce market or to support further researches on the field of digital support of languages. This article presents a complete description of the methodological elements involved in the production of an unprecedented set of indicators of the presence in the Internet of the 329 languages with more than 1 million L1 speakers. A special emphasis is given to the treatment of the comprehensive set of biases involved in the process, either from the method or the various sources used in the modeling process. The biases related to other sources providing similar data are also discussed, and in particular, it is shown how the lack of consideration of the high level of multilingualism of the Web leads to a huge overestimation of the presence of English. The detailed list of sources is presented in the various annexes. For the first time in the history of the Internet, the production of indicators about virtual presence of a large set of languages could allow progress in the fields of economy of languages, cyber-geography of languages and language policies for multilingualism.
How to improve our understanding of solar wind-magnetosphere interactions on the basis of the statistical evaluation of the energy budget in the magnetosheath?
Authors: Vörös Zoltán , Roberts Owen W. , Yordanova Emiliya , Sorriso-Valvo Luca , Nakamura Rumi , Narita Yasuhito , Schmid Daniel , Plaschke Ferdinand , Kis Árpád
Published: May 2023

Solar wind (SW) quantities, referred to as coupling parameters (CPs), are often used in statistical studies devoted to the analysis of SW–magnetosphere–ionosphere couplings. Here, the CPs and their limitations in describing the magnetospheric response are reviewed. We argue that a better understanding of SW magnetospheric interactions could be achieved through estimations of the energy budget in the magnetosheath (MS), which is the interface region between the SW and magnetosphere. The energy budget involves the energy transfer between scales, energy transport between locations, and energy conversions between electromagnetic, kinetic, and thermal energy channels. To achieve consistency with the known multi-scale complexity in the MS, the energy terms have to be complemented with kinetic measures describing some aspects of ion–electron scale physics.
The role of marketing stimuli and attitude in determining post-COVID buying decisions toward organic food products: evidence from retail consumers in Beijing, China
Authors: Jiang Yuling , Abdullah Siti Intan Nurdiana Wong , Lim Bernard Heng Jit , Wang Ruiyun , Phuah Kit Teng
Published: May 2023

Customers are diverse, and their preferences have changed tremendously, especially post-pandemic, as many business organizations are facing major challenges in meeting those needs. Therefore, an in-depth understanding of consumer buying decisions plays a vital role to develop effective strategies. The aim of this study is to determine whether the marketing mix (4Ps) elements and their attitude have a significant effect on organic food product buying decisions among retail consumers in Beijing, China. A quantitative method was used in this study, whereby 334 questionnaires were physically collected randomly from walk-in customers at Walmart, Darunfa, and Yonghui supermarkets in Beijing, China. Based on the results of the hypothesis test using PLS-SEM, it was confirmed that product, place and promotional strategies had a significant positive relationship with attitude and consumer buying decisions. Interestingly, price had no effect and income was not a moderator. The results of this study provided relevant suggestions to marketing practitioners, especially organic food producers and retail stores on developing and implementing marketing strategies effectively to address changing consumer preferences.
Changes in advanced protein structure during dense phase carbon dioxide induced gel formation in golden pompano surimi correlate with gel strength
Authors: Duan Weiwen , Qiu Hui , Htwe Kyi Kyi , Wei Shuai , Liu Yang , Wang Zefu , Sun Qinxiu , Han Zongyuan , Xia Qiuyu , Liu Shucheng
Published: May 2023

Changes in protein structure are closely related to gel strength. Dense phase carbon dioxide (DPCD) treatment is an excellent non-thermal food processing method that can be used to induce gel formation in surimi. The sensory, water holding capacity and gel strength of DPCD induced gels are superior to heat-induced gels. Fourier-transform infrared spectroscopy was used to investigate the role of DPCD in the quality of golden pompano surimi gels and changes in protein structure. The intermolecular forces of surimi gels were analyzed in terms of ionic and hydrogen bonds, disulfide covalent and non-disulfide covalent bonds, as well as hydrophobic interactions. Correlation analysis was used to investigate the relationship between the changes in advanced protein structure and gel strength during DPCD-induced gel formation in golden pompano surimi. The results showed that the α-helix and random coil levels of surimi gel were significantly decreased (p < 0.05), while the β-sheet and β-turn content was significantly increased (p < 0.05). The number of ionic and hydrogen bonds in gel proteins decreased significantly (p < 0.05), while the hydrophobic interactions, and disulfide and non-disulfide covalent bonds increased significantly (p < 0.05) after DPCD treatment. Correlation analysis showed that β-sheets, β-turns, hydrophobic interactions, and disulfide and non-disulfide covalent bonds were strongly positively correlated with gel strength, whereas α-helices, random coils, and ionic and hydrogen bonds were strongly negatively correlated with gel strength. Therefore, the α-helix and random coil structures of surimi gels were transformed into β-sheet and β-turn structures after DPCD treatment. Hydrophobic interactions, and disulfide and non-disulfide covalent bonds were the main intermolecular forces during the DPCD-induced gel formation of surimi. Ionic and hydrogen bonds were not the main intermolecular forces. The results provide fundamental data for elucidating the mechanism of DPCD-induced protein gel formation.
Hierarchical Modelling for CO2 Variation Prediction for HVAC System Operation
Authors: Shaer Ibrahim , Shami Abdallah
Published: May 2023

Residential and industrial buildings are significant consumers of energy, which can be reduced by controlling their respective Heating, Ventilation, and Air Conditioning (HVAC) systems. Demand-based Ventilation (DCV) determines the operational times of ventilation systems that depend on indoor air quality (IAQ) conditions, including CO2 concentration changes, and the occupants’ comfort requirements. The prediction of CO2 concentration changes can act as a proxy estimator of occupancy changes and provide feedback about the utility of current ventilation controls. This paper proposes a Hierarchical Model for CO2 Variation Predictions (HMCOVP) to accurately predict these variations. The proposed framework addresses two concerns in state-of-the-art implementations. First, the hierarchical structure enables fine-tuning of the produced models, facilitating their transferability to different spatial settings. Second, the formulation incorporates time dependencies, defining the relationship between different IAQ factors. Toward that goal, the HMCOVP decouples the variation prediction into two complementary steps. The first step transforms lagged versions of environmental features into image representations to predict the variations’ direction. The second step combines the first step’s result with environment-specific historical data to predict CO2 variations. Through the HMCOVP, these predictions, which outperformed state-of-the-art approaches, help the ventilation systems in their decision-making processes, reducing energy consumption and carbon-based emissions.
Time-Efficient Identification Procedure for Neurological Complications of Rescue Patients in an Emergency Scenario Using Hardware-Accelerated Artificial Intelligence Models
Authors: Ahammed Abu Shad , Ezekiel Aniebiet Micheal , Obermaisser Roman
Published: May 2023

During an emergency rescue operation, rescuers have to deal with many different health complications like cardiovascular, respiratory, neurological, psychiatric, etc. The identification process of the common health complications in rescue events is not very difficult or time-consuming because the health vital symptoms or primary observations are enough to identify, but it is quite difficult with some complications related to neurology e.g., schizophrenia, epilepsy with non-motor seizures, or retrograde amnesia because they cannot be identified with the trend of health vital data. The symptoms have a wide spectrum and are often non-distinguishable from other types of complications. Further, waiting for results from medical tests like MRI and ECG is time-consuming and not suitable for emergency cases where a quick treatment path is an obvious necessity after the diagnosis. In this paper, we present a novel solution for overcoming these challenges by employing artificial intelligence (AI) models in the diagnostic procedure of neurological complications in rescue situations. The novelty lies in the procedure of generating input features from raw rescue data used in AI models, as the data are not like traditional clinical data collected from hospital repositories. Rather, the data were gathered directly from more than 200,000 rescue cases and required natural language processing techniques to extract meaningful information. A step-by-step analysis of developing multiple AI models that can facilitate the fast identification of neurological complications, in general, is presented in this paper. Advanced data analytics are used to analyze the complete record of 273,183 rescue events in a duration of almost 10 years, including rescuers’ analysis of the complications and their diagnostic methods. To develop the detection model, seven different machine learning algorithms-Support Vector Machine (SVM), Random Forest (RF), K-nearest neighbor (KNN), Extreme Gradient Boosting (XGB), Logistic Regression (LR), Naive Bayes (NB) and Artificial Neural Network (ANN) were used. Observing the model’s performance, we conclude that the neural network and extreme gradient boosting show the best performance in terms of selected evaluation criteria. To utilize this result in practical scenarios, the paper also depicts the possibility of embedding such machine learning models in hardware like FPGA. The goal is to achieve fast detection results, which is a primary requirement in any rescue mission. An inference time analysis of the selected ML models and VTA AI accelerator of Apache-TVM machine learning compiler used for the FPGA is also presented in this research.
Numerical Modeling of Thermal Behavior during Lunar Soil Drilling
Authors: Zhao Deming , Cheng Zhisheng , Zhang Weiwei , Cui Jinsheng , Wang He
Published: May 2023

This paper presents a detailed thermal simulation analysis of the drilling process for icy soil in the lunar polar region. The aim is to investigate the temperature changes that occur in the debris removal area during the drilling process. We developed a multi-level particle size simulation model that includes a thermal sieve based on geometric constraints to evaluate the influence of specific heat capacity and thermal conductivity on particle temperature. Using the central composite design method, we carried out the simulation test design and analyzed the average temperature difference of particles within and outside the range of the thermal sieve. The parameters of the discrete element model were determined by comparing the temperature of the debris removal zone in the lunar environment with the temperature simulated by the discrete element method. The results show that the thermal conductivity of the sieve ranges from 100 to 400 W/m, and the average temperature inside the thermal sieve is negatively related to the specific heat capacity. The temperature deviation of the chip removal area is ±10 °C, which is consistent with the temperature deviation observed in the lunar environment and the lunar icy regolith drilling test. Furthermore, the addition of the thermal sieve to the multi-stage particle size simulation modeling significantly reduces the calculation time by 86%. This reduction in computational time may potentially increase the efficiency of drilling operations in the future. Our study provides insights into the thermal behavior of lunar icy regolith during drilling, and proposes a numerical model of heat transfer with a thermal sieve that can effectively reduce computational time while ensuring accurate temperature calculations.
Assessment and Principles of Environmentally Sustainable Food and Agriculture Systems
Authors: Çakmakçı Ramazan , Salık Mehmet Ali , Çakmakçı Songül
Published: May 2023

Feeding the world depends on protecting our valuable ecosystems and biodiversity. Currently, increasing public awareness of the problems posed by the current industrialized food system has resulted in increased support for the creative market for economically, socially, and ecologically sustainable food production systems and enhanced demands for variations in agricultural policies and regulations. In food production, the restoration and protection of ecosystems and sustainable food systems must be given priority, which requires a forward-looking rational management strategy and fundamental changes in patterns and practices of economic development, product, and production. Food systems should be redesigned to have a neutral and positive environmental impact, as well as ensure healthy nutrition and food safety, and low environmental impact strategies should become a priority. This review paper aims to discuss, build, guide and evaluate sustainable food systems, principles, and transition strategies such as agroecological, organic, biodynamic, regenerative, urban, and precision agriculture, which are imperative visions for the management of agriculture and food production. To this end, we analyzed the evolution of the established strategies to develop sustainable agriculture and food systems, and we created assessment of key sustainability issues related to food, environment, climate, and rural development priorities and resource use practices.
Can Drones Help Smallholder Farmers Improve Agriculture Efficiencies and Reduce Food Insecurity in Sub-Saharan Africa? Local Perceptions from Malawi
Authors: McCarthy Christopher , Nyoni Yamikani , Kachamba Daud Jones , Banda Lumbani Benedicto , Moyo Boyson , Chisambi Cornelius , Banfill James , Hoshino Buho
Published: May 2023

Smallholder farmers in sub-Saharan Africa play a vital role in achieving food security and nutrition, yet they are often overlooked by development policies and lack access to the technology and information needed to increase their agricultural productivity. This is particularly true in Malawi, where smallholder farmers make up over 80% of the population and face a range of risks and challenges, including vulnerability to climate change, that threaten their livelihoods, food security, and nutrition. While drones and precision agriculture technology have had a significant impact on agriculture in high-income countries, their application by smallholder farmers in low-income countries is not well understood. This study, conducted in 2022, examines how drones can assist smallholder farmers in increasing their agricultural productivity and food security in Malawi. It explores how smallholders perceive the use of drones and the potential benefits and limitations of using drones in their farming operations. A unique aspect of this study aims to understand smallholders’ perceptions of open data and data privacy. The results show that when smallholder farmers interact with drone data, they have a better understanding of their farm and are able to make more informed decisions that use fewer inputs and reduce production costs. Overall, this study demonstrates the potential for drones to assist smallholder farmers improve their on-farm knowledge, increase agricultural productivity, and mitigate the risks and challenges they face, leading to improved livelihoods and a more sustainable and secure food supply. Policymakers can promote the adoption of drone technology among smallholder farmers by developing policies that encourage public–private partnerships to create affordable, locally adapted drone technologies and programs that meet their unique needs, while also ensuring responsible use of drones in agriculture through regulations that address concerns about data privacy and security.
Selection of New Sugarcane Genotypes for Sandy Soils in Florida with Enhanced Sucrose Content
Authors: Coto Arbelo Orlando , Momotaz Aliya , Sandhu Hardev S. , Sood Sushma , Davidson Wayne , Baltazar Miguel , Zhao Duli
Published: May 2023

The selection of sugarcane genotypes with high sucrose content and good ratooning ability (RA) is an important objective of the Canal Point breeding program to improve the current profitability levels of the Florida sugarcane industry. In this study, thirteen test sugarcane genotypes and three checks were evaluated in three sand locations, Pahokee Produce Inc. (PP), Townsite farm (TS), and Lykes Brothers Inc. (PF), during three crop cycles (plant cane, first ratoon, and second ratoon). Multi-environment best linear unbiased predictors (BLUPs) were highly significant for commercially recoverable sucrose (CRS) and not significant for cane yield (CY) among genotypes. The ANOVA based on the RA values produced significant genotypic effects but a reduced RA diversity among the genotypes. The simultaneous selection for BLUP_CRS, BLUP_CY yield, and RA identified CP 14-4165 and CP 13-2340 as the most outstanding genotypes. The BLUP_GGE biplots method showed that the PP location was the most discriminative for BLUP_CY, whereas the TS was the ideal location. For BLUP_CRS, the three locations had similar abilities to discriminate genotypes and were positively and strongly correlated. The which-won-where graph indicated that CP 13-2340 showed the highest BLUP_CRS levels in TS and PP locations, while CP 14-4165 and CP 14-4588 were the top genotypes in the PF location. The results suggest that selecting genotypes with high CRS values is possible without compromising the genotype discrimination for CY. CP 14-4165 and CP 13-2340 are resistant to most diseases and genetically diverse.
Discovery of Oscheius myriophilus (Nematoda: Rhabditidae) in Gastropods and Its Similar Virulence to Phasmarhabditis papillosa against Arion vulgaris, Deroceras reticulatum, and Cernuella virgata
Authors: Laznik Žiga , Trdan Stanislav , Tóth Tímea , Ádám Szabolcs , Lakatos Tamás , Majić Ivana
Published: May 2023

Between July and September 2021, researchers collected 100 specimens of the Spanish slug, Arion vulgaris, and dissected their cadavers to examine them for parasitic nematodes. Molecular techniques were used to identify the nematodes, which confirmed the presence of Oscheius myriophilus, marking the first recorded instance of this nematode in a gastropod host. To test the virulence of Slovenian strains of O. myriophilus and Phasmarhabditis papillosa and their effects on the feeding behavior of the Spanish slug, grey field slug (Deroceras reticulatum), and vineyard snail (Cernuella virgata), laboratory bioassays were conducted using nematodes grown in vivo. Nematodes were applied at various doses ranging from 10 to 500 nematodes/gastropod. The results showed that O. myriophilus and P. papillosa caused significant mortality (82.5% ± 2.5% at 15 °C) of the Spanish slug while being less effective against the vineyard snail and grey field slug. Nematodes were more virulent at a lower temperature (15 °C) than at the higher temperature (20 °C) tested in the experiment. Additionally, both nematode species significantly reduced gastropod herbivory. The potential use of O. myriophilus and P. papillosa as biological control agents against gastropods is discussed.
Enhancing Rice Yield and Nitrogen Utilization Efficiency through Optimal Planting Density and Reduced Nitrogen Rates
Authors: Zhu Haijun , Wen Tian , Sun Manwen , Ali Izhar , Sheteiwy Mohamed S. , Wahab Abdul , Tan Weijian , Wen Can , He Xiaoe , Wang Xuehua
Published: May 2023

Rice yields can only be achieved by selecting a high-quality population. Nitrogen rates and transplanting density play a significant role in determining population quality and yield. Field experiments were conducted in Heshan District, Hunan Province, China, to explore suitable nitrogen rates and appropriate transplanting densities for rice production in 2021 and 2022. In this study, three levels of nitrogen, e.g., N1, N2, and N3 (0, 150, and 210) kg ha−1, and three transplanting densities, e.g., B1, B2, and B3 (1, 2, and 3) seedlings per hill were used to study their individual or combined effects on rice (Taiyou 390) population quality, yield, and yield components, nitrogen-related enzyme activities, and nitrogen absorption and utilization efficiency. The results showed that N2B3 had the highest yield, with an average yield of 9.30 t ha−1 in two years, which was 3.7~49.6% higher than other treatments. This increase was attributed to higher dry matter accumulation (1538.22 g m−2) and effective panicle number (435.2 × 104 ha−1), influenced by increased nitrate reductase activity at the booting stage and glutamine synthase at the heading stage, along with maintenance of higher SPAD value and leaf area index. Nitrogen rates and transplanting densities significantly affected nitrogen use efficiency, with the contribution rate of nitrogen fertilizer to yield decreasing as nitrogen rates increased. However, N2B3 improved nitrogen use efficiency and stabilized rice yield by reducing nitrogen fertilizer application. This study suggested that N2B3 treatment could enhance rice yield by improving plant nitrogen use efficiency under low nitrogen supplementation.
Accurate Measurement of Frozen Soil Depth Based on I-TDR
Authors: Qin Haoqin , Mu Zhiquan , Jia Xingyue , Kang Qining , Li Xiaobin , Xu Jinghui
Published: May 2023

In this study, a new method for determining the depth of frozen soil, Impulse Response Time Domain Reflectometry, is discussed. This method uses the principle of impedance measurement and the law of time–frequency domain convolution to convert the frequency-domain reflection signal into a time-domain signal and accurately determines the soil freezing front by measuring the difference between the impedance of frozen soil and unfrozen soil. The advantage of this method is that it solves the problems of small bandwidth, long rising edge time, and large measurement errors in the traditional TDR method to effectively improve the measurement accuracy of the soil-freezing front. Under laboratory conditions, soils of different textures (sand, loess, black soil, and red soil) were selected for experimental determination, and the results showed that compared with the traditional TDR method, the RMSE of the I-TDR method was small, and the method was applicable under different soil texture conditions, which could provide a new method for monitoring frozen soil in cold areas. In addition, the application of this method has important guiding significance for improving the efficiency of winter irrigation water, especially for guiding agricultural production, farmland irrigation, drainage engineering construction, meteorological frozen soil monitoring, and other aspects in cold and arid areas.
Diversity, Community Structure, and Potential Functions of Root-Associated Bacterial Communities of Different Wheat (Triticum aestivum) Cultivars under Field Conditions
Authors: Acuña Jacquelinne J. , Rilling Joaquin I. , Inostroza Nitza G. , Manquian Javiera , Zhang Qian , Gupta Vadakattu V. S. R. , Jorquera Milko A.
Published: May 2023

The wheat (Triticum aestivum) microbiome is essential to its growth and adaptation under the current climatic crisis. Wheat breeding programs are often mainly focused on obtaining more resistant cultivars; thus, plant genotype-by-microbiome interactions have gained attention. In this sense, local wheat cultivars represent a unique opportunity to examine how bacterial communities are recruited and support plant growth under field conditions. In this study, we explored the diversity, community structure, and potential functions of root-associated bacterial communities of four Chilean wheat (Triticum aestivum) cultivars under field conditions through Illumina MiSeq. Analyses showed that Proteobacteria was the most abundant phylum in root endosphere (51.1 to 74.4%) and rhizosphere samples (39.3 to 44.9%) across wheat cultivars. Significant differences (p ≤ 0.05) in alpha and beta diversity were observed in root endosphere and rhizosphere samples, independently of wheat genotypes. Potassium was identified as the main factor driving the rhizosphere microbiomes of wheat. A higher proportion of shared operational taxonomic units (OTUs) were found in rhizosphere (mainly Pseudomonas, Flavobacterium, and Janthinobacterium) compared with root endosphere (dominated by Delftia, Acinetobacter, Stenotrophomonas, Kaistobacter) samples across all cultivars. Analyses of larger predicted functional activities revealed that chemoheterotrophy and aerobic chemoheterotrophy were more observed in the root endosphere environment, whereas among the minor functions, nitrogen cycling was the more predicted trait, related to rhizosphere samples. A co-occurrence analysis revealed complex bacterial interactions in wheat cultivars’ niche microbiomes identifying three (Comamonadaceae, Enterobacteraceae, Micrococcaceae) and four (Corynebacteraceae, Dermabacteraceae, Xanthomonadaceae, Staphylococcaceae) families as keystone taxa for the root endosphere and rhizosphere, respectively. It is suggested that such findings on the differences in root microbiomes associated with wheat cultivars under field conditions would help to develop new cultivars with abilities to recruit specific bacterial communities.
H2O2 Participates in the Induction and Formation of Potato Tubers by Activating Tuberization-Related Signal Transduction Pathways
Authors: Lei Chunxia , Ye Mingwang , Li Canhui , Gong Ming
Published: May 2023

Reactive oxygen species (ROS), especially hydrogen peroxide, H2O2, act as signaling molecules to widely mediate growth, development, and stress response of plants. In the present study, internal ROS accumulation, effects of exogenous H2O2 treatment, the expression of the key tuberization-related genes, and the effect of knockout of Solanum tuberosum self-pruning 6A (StSP6A) on H2O2-induced tuber formation were investigated to elucidate whether and how H2O2 is involved in induction and formation of potato tubers using two diploid landraces, Solanum phureja and S. ajanhuiri. The results showed that there was a significant accumulation of ROS (including H2O2, superoxide anion, O2−, and total ROS) during tuber induction and formation in stolons/tubers, especially in the hook-like subapical part of stolons prior to tuberization, as detected by staining observation and quantitative measurement. Furthermore, exogenous H2O2 treatment significantly enhanced percentage of tuber formation. By contrast, addition of either the ROS inhibitor diphenyleneiodonium chloride (DPI) or H2O2 scavenger catalase (CAT) resulted in a decline of tuber formation. In addition, expression analysis of nine key tuberization-related genes demonstrated that the H2O2-induced tuberization could be associated with H2O2-controlled regulation of these tuberization- and signaling-pathway-related genes, especially StSP6A, which was dramatically up-regulated during the early stage of tuber induction and H2O2 treatment. When StSP6A was knocked out by CRISPR-Cas9-mediated genome editing, the tuberization frequency of StSP6A null-mutants became significantly lower at various H2O2 concentration treatments. These findings indicate that H2O2 accumulation in stolons might play an important role by acting as a signaling molecule to initiate tuber induction, H2O2-induced tuber formation is triggered by regulating the tuberization-related gene expression and activating signal transduction pathways, and StSP6A is a pivotal player in H2O2-induced tuber formation in potato.
Comparative Transcriptome Analysis Reveals the Innate Immune Response to Mycoplasma gallisepticum Infection in Chicken Embryos and Newly Hatched Chicks
Authors: Zou Mengyun , Wang Tengfei , Wang Yingjie , Luo Ronglong , Sun Yingfei , Peng Xiuli
Published: May 2023

Mycoplasma gallisepticum (MG) is a major cause of chronic respiratory diseases in chickens, with both horizontal and vertical transmission modes and varying degrees of impact on different ages. The innate immune response is crucial in resisting MG infection. Therefore, this study aimed to investigate the innate immune response of chicken embryos and newly hatched chicks to MG infection using comparative RNA-seq analysis. We found that MG infection caused weight loss and immune damage in both chicken embryos and chicks. Transcriptome sequencing analysis revealed that infected chicken embryos had a stronger immune response than chicks, as evidenced by the higher number of differentially expressed genes associated with innate immunity and inflammation. Toll-like receptor and cytokine-mediated pathways were the primary immune response pathways in both embryos and chicks. Furthermore, TLR7 signaling may play an essential role in the innate immune response to MG infection. Overall, this study sheds light on the development of innate immunity to MG infection in chickens and can help in devising disease control strategies.
Evolution of Avian Eye Size Is Associated with Habitat Openness, Food Type and Brain Size
Authors: Liu Yating , Jiang Ying , Xu Jiliang , Liao Wenbo
Published: May 2023

The eye is the primary sensory organ that obtains information from the ecological environments and specifically bridges the brain with the extra environment. However, the coevolutionary relationships between eye size and ecological factors, behaviours and brain size in birds remain poorly understood. Here, we investigate whether eye size evolution is associated with ecological factors (e.g., habitat openness, food type and foraging habitat), behaviours (e.g., migration and activity pattern) and brain size among 1274 avian species using phylogenetically controlled comparative analyses. Our results indicate that avian eye size is significantly associated with habitat openness, food type and brain size. Species living in dense habitats and consuming animals exhibit larger eye sizes compared to species living in open habitats and consuming plants, respectively. Large-brained birds tend to possess larger eyes. However, migration, foraging habitat and activity pattern were not found to be significantly associated with eye size in birds, except for nocturnal birds having longer axial lengths than diurnal ones. Collectively, our results suggest that avian eye size is primarily influenced by light availability, food need and cognitive ability.
Cytotoxicity and Antibiofilm Activity of Silver-Polypropylene Nanocomposites
Authors: Bellisario Denise , Santo Loredana , Quadrini Fabrizio , Hassiba Maryam , Bader Nour , Chowdhury Shazeda H. , Hassan Mohammad K. , Zughaier Susu M.
Published: May 2023

The development of biocompatible nanomaterials that interface with human skin and tissue is critical for advancing prosthetics and other therapeutic medical needs. In this perspective, the development of nanoparticles with cytotoxicity and antibiofilm properties and biocompatibility characteristics are important. Metallic silver (Ag) exhibits good biocompatibility, but it is often challenging to integrate it into a nanocomposite without compromising its antibiofilm properties for optimal applications. In this study, new polymer nanocomposites (PNCs) with ultra-low filling content (0.0023–0.046 wt%) of Ag nanoplates were manufactured and tested. The cytotoxicity and antibiofilm activity of different composites with polypropylene (PP) matrix were examined. At first, PNCs surface were analyzed by means of AFM (atomic force microscopy) with phase contrast evaluation and FTIR (Fourier-transform infrared spectroscopy) to study the Ag nanoplates distribution. Subsequently, the cytotoxicity and growth properties of biofilms were assessed by MTT assay protocol and detection of nitric oxide radicals. Antibacterial and antibiofilm activities were measured against Gram-positive bacteria (Staphylococcus aureus) and Gram-negative bacteria (K. pneumoniae). The PNCs with silver exhibited antibiofilm activity although they did not inhibit regular planktonic bacterial growth. Moreover, the PNCs were not cytotoxic to mammalian cells and did not induce significant immune response. These features reveal the potential of the PNCs developed in this study for usage in fabrication of prosthetics and other smart structures for biomedical applications.
Optimal Load Frequency Control of a Hybrid Electric Shipboard Microgrid Using Jellyfish Search Optimization Algorithm
Authors: Karnavas Yannis L. , Nivolianiti Evaggelia
Published: May 2023

This paper examines the critical topic of load frequency control (LFC) in shipboard microgrids (SMGs), which face challenges due to low system inertia and the intermittent power injection of renewable energy sources. To maintain a constant frequency (even under system uncertainties), a robust and well-tuned controller is required. In this paper, a study was conducted first by examining the performance of three different controller architectures, in order to determine which is the most-appropriate for the multi-energy SMG system. The time delays that occur due to communication links between the sensors and the controller were also considered in the analysis. The controllers were tuned using a very recent bio-inspired optimization algorithm called the jellyfish search optimizer (JSO), which has not been used until recently in LFC problems. To assess the tuning efficiency of the proposed optimization algorithm, the SMG’s frequency response results were comprehensively compared to the results obtained with other bio-inspired optimization algorithms. The results showed that the controllers with gains provided by the JSO outperformed those tuned with other bio-inspired optimization counterparts, with improvements in performance ranging from 19.13% to 93.49%. Furthermore, the robustness of the selected controller was evaluated under various SMG operational scenarios. The obtained results clearly demonstrated that the controller’s gains established in normal conditions do not require retuning when critical system parameters undergo a significant variation.
A Hybrid Watermarking Approach for DICOM Images Security
Authors: Tayachi Mayssa , Nana Laurent , Pascu Anca Christine , Benzarti Faouzi
Published: May 2023

A hybrid watermarking approach for DICOM medical image protection is proposed in this paper. The medical image is first separated into two parts: the region used for the diagnosis, called Region Of Interest (ROI), and the remaining, called Region Of Non-Interest (RONI). The patient’s name is then extracted from the header of the DICOM image and pertinent features from the ROI. These data are therefore used to construct a watermark based on a Jacobian model. On the one hand, this watermark is used for the zero watermarking of the ROI. On the other hand, it is divided into blocks that are embedded in the RONI using a linear interpolation technique. Experiment results show a good performance of the proposed approach. The average values of SSIM, PSNR, NC, and BER are respectively 0.98, 71 dB, 0.98, and 0.0054, and the watermark embedding and extraction times do not exceed 12 s.
Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features
Authors: Calderon-Uribe Uriel , Lizarraga-Morales Rocio A. , Guryev Igor V.
Published: May 2023

The detection of faults in induction motors has been one of the main challenges to the industry in recent years. An effective fault detection method is fundamental to ensure operational security and productivity. Different models for intelligent fault diagnosis have been recently proposed. However, not all of them are accessible for some manufacturing processes because of the black-box approach, the complexity of hyperparameter tuning, high-dimensionality feature vectors, and the need for sophisticated computational resources. In this paper, a method for the detection of an unbalance fault in induction motors based on a low-dimensional feature vector and a low-complexity classification approach is proposed. The feature vector presented in this manuscript is based on texture features, which are a basic tool for image processing and image understanding. Nevertheless, texture features have not been explored as a powerful instrument for induction motor fault analysis. In this approach, texture features are used to analyze a set of vibration signals belonging to two different classes: an unbalanced motor and a healthy motor. Training-validation and testing stages are developed to build and evaluate the performance of the classifier, respectively. The results show higher accuracy and lower training time in comparison with different state-of-the-art approaches.