bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of teaching effect of first-aid comprehensive simulation-based education in clinical medical students
Authors: Peng Mian , Su Ning , Hou Rui , Geng Huijuan , Cai Fangfang , Zhong Weixiong , Zhang Weifang , Zhong Jingxing , Yang Zhengyue , Cao Weiling
Published: August 2022

Background

Although students mastered the composition skills, they lack of the ability to effectively integrate these composition skills in real clinical situations. To address the problem, we set up different levels of situational simulation training for medical students in grades 2–4, and evaluate the teaching effect of first-aid situation comprehensive simulation-based education (SBE) on clinical medical students.

Methods

The medical students in Grade 2, 3, and 4 received different situational SBE, respectively. The 2nd-year medical students received a single skill module which included cardiopulmonary resuscitation, endotracheal intubation, and electric defibrillation training. The 3rd-year medical students received a single subject module which included cardiovascular and respiratory system training. The 4th-year medical students received the integrated multidisciplinary module which combined first-aid skills, clinical thinking, and teamwork training. The primary outcome was the expert evaluation and peer evaluation. The secondary outcome was students' satisfaction questionnaire response. In our training, we arranged an adequate teaching staff for intensive training and timely feedback (the student–teacher ratio of 5:1), adequate time for repetitive practice (Each SBE was carried out within 4 h), curriculum design, and integration from real cases by clinicians, realistic computer-driven mannequins to ensure simulation fidelity, providing a different difficult level of SBE to different grades of students, and pre- and post-tests for outcome measurement.

Results

In all of the single skill module, single subject module or comprehensive disciplines module, the scores in the expert evaluation and peer assessment after the training were significantly higher than before the training, and the differences were statistically significant (p < 0.05). The integrated subject training, although having the lowest pre—and post-test marks, had the largest increase in score.

Conclusion

The first aid comprehensive simulation-based education in grade 2–4 clinical medical students, basing on timely feedback, repetitive practice, curriculum integration, simulation fidelity, and outcome measurement are effective in improving the students' proficiency in managing the real emergencies.
Psychosocial response to the COVID-19 pandemic in Panama
Authors: Oviedo Diana C. , Pinzón María Sofía , Rodríguez-Araña Sofía , Tratner Adam E. , Pauli-Quirós Elianne , Chavarría Carlos , Posada Rodríguez Camilo , Britton Gabrielle B.
Published: August 2022

Background

The impact of the COVID-19 pandemic and the associated restrictions on mental health is being studied.

Objective

To analyze the psychosocial response to the COVID-19 pandemic in adults residing in Panama.

Methods

A community sample of 480 adult residents of Panama completed a survey that included sociodemographic questions, COVID-19 related questions (e.g., health concerns regarding the virus, knowledge and behaviors in biosafety) and scales of stress, anxiety, depression, prosocial behavior, resilience, perceived social support, and insomnia.

Results

Most of the participants (>60%) reported being negatively affected by the pandemic. Women experienced greater depression, anxiety, and stress symptoms than men, and age was negatively associated with depression, anxiety, and stress symptoms. Self-perceived health status and self-perceived social support were negatively associated with depression, anxiety, and stress symptoms. Self-perceived social isolation was positively associated with depression, anxiety, and stress symptoms. Psychiatric illness and insomnia were positively associated with depression, anxiety, and stress symptoms, whereas psychological resilience was negatively associated with depression, anxiety, and stress symptoms.

Discussion

These results corroborate other studies regarding COVID-19 and mental health. This study highlights the need for specific prevention and intervention mechanisms related to the COVID-19 pandemic in different population groups. This is the first report of the psychological impact of COVID-19 in the general Panamanian population and one of the only studies in the Latin American region and, therefore, contributes to research in the Latino population and lower-middle income countries.
The Chronic Kidney Disease Epidemiology Collaboration equations perform less well in an older population with type 2 diabetes than their non-diabetic counterparts
Authors: Jiang Shimin , Zhang Danyang , Li Wenge
Published: August 2022

Objectives

The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations are based on creatinine alone (CKD-EPIcr), cystatin C alone (CKD-EPIcys) and combined creatinine and cystatin C (CKD-EPIcr-cys). It remains unclear whether these equations perform differently in older adults with type 2 diabetes than they do in non-diabetic older individuals.

Methods

This single-center cross-sectional study was performed in adults aged ≥ 65 years between January 2019 and December 2021. Glomerular filtration rate (GFR) was measured by technetium-99m-diethylene triamine pentaacetic acid (99mTc-DTPA) renal dynamic imaging. The bias (difference between measured and estimated GFR), precision [interquartile range (IQR) of the median difference between measured GFR and estimated GFR] and accuracy P30 (percentage of estimated GFR within 30% of measured GFR) were considered the criteria of equation performance.

Results

Finally, 476 participants were enrolled, including 243 adults with type 2 diabetes and 233 non-diabetic adults. The mean age of the included participants was 71.69 ± 6.4 years and 262 (55%) were male. The mean measured GFR was 49.02 ± 22.45 ml/min/1.73 m2. The CKD-EPIcr-cys equation showed significantly greater bias and lower accuracy (P30) in individuals with diabetes than in the non-diabetic group (median bias, 4.08 vs. 0.41 ml/min/1.73 m2, respectively, p < 0.05; P30, 63.78% vs. 78.54%, respectively, p < 0.05). The precision IQR indicated that CKD-EPIcr-cys had also lower precision in individuals with diabetes than in the non-diabetic controls (17.27 vs. 15.49 ml/min/1.73 m2, respectively). Similar results were observed for CKD-EPIcr and CKD-EPIcys equations. The P30 of all three equations failed to reach 80% in diabetic and non-diabetic groups.

Conclusions

The performance of the CKD-EPI equations was lower in a group of patients aged ≥ 65 years with type 2 diabetes than in non-diabetic counterparts. However, each equation still had limitations regarding accuracy in older adults with or without diabetes.
Improved building-specific flood risk assessment and implications of depth-damage function selection
Authors: Gnan Ehab , Friedland Carol J. , Rahim Md Adilur , Mostafiz Rubayet Bin , Rohli Robert V. , Orooji Fatemeh , Taghinezhad Arash , McElwee Jasmin
Published: August 2022

Average annual loss (AAL) is traditionally used as the basis of assessing flood risk and evaluating risk mitigation measures. This research presents an improved implementation to estimate building-specific AAL, with the flood hazard of a building represented by the Gumbel extreme value distribution. AAL is then calculated by integrating the area under the overall loss-exceedance probability curve using trapezoidal Riemann sums. This implementation is compared with existing AAL estimations from flood risk assessment. A sensitivity analysis is conducted to examine the variability in AAL results based on depth-damage function (DDF) choice. To demonstrate the methodology, a one-story single-family residence is selected to assess the financial benefits of freeboard (i.e., increasing lowest floor elevations). Results show that 1 ft. of freeboard results in annual flood risk reduction of over $1,000, while 4 ft of freeboard results in annual flood risk reduction of nearly $2,000. The sensitivity result suggests that the DDF selection is critical, as a large proportion of flood loss is counted below the top of the first floor. The findings of this paper will enhance DDF selection, improve flood loss estimates, encourage homeowners and communities to invest in flood mitigation, and provide government decision-makers with improved information when considering building code changes.
Market access and dietary diversity: A spatially explicit multi-level analysis in Southern and Western Kenya
Authors: Milner Daniel , Wolf Levi , Wijk Mark Van , Hammond James
Published: August 2022

The risk of malnutrition, particularly micronutrient deficiency, is high in large parts of Sub-Saharan Africa for smallholder farmers. Access to diverse and nutritious food is a key component of food security, and a major development objective. It is widely accepted that good access to markets can play a key role in improving nutrition at the foodshed level. However, the magnitude and even the direction of the effect of increased market access on household dietary diversity (and thus food security) is not universal, with studies showing divergent results. One reason for these divergences may be that models do not account for place-based mediation effects, that is, farmers' local context can affect whether (and the extent to which) access to market is important to their nutrition. Drawing on household survey data from 914 Kenyan smallholder farmers from ten counties in South and West Kenya, we used a novel methodology to evaluate the role of market access in determining household dietary diversity. This methodology combines the clustering of households along places with similar characteristics and multi-level regression analysis to understand the place based variation in effects of different factors on dietary diversity. We found that, depending on how “access to market” is measured, there can be significant impacts on dietary diversity, and this is mediated by farm characteristics. For small farms with already good market access, higher diet diversity is associated with cultivating larger areas and owning larger livestock holdings, but not with easier market access. For isolated larger farms with a focus on livestock production, higher diet diversity is associated with easier market access (i.e., proximity to road), as well as greater livestock diversity. For medium-sized farms with good market access, diet diversity is mildly correlated with easier market access (i.e., proximity to road) but significantly associated with greater crop diversity. The need to account for place-based mediating effects is clearly important and highlights an exigency for greater use and development of localized models that can capture the extent to which effects might change when contexts change.
Identification of Hub Genes of Lung Adenocarcinoma Based on Weighted Gene Co-Expression Network in Chinese Population
Authors: Xie Yuning , Wu Hongjiao , Hu Wenqian , Zhang Hongmei , Li Ang , Zhang Zhi , Ren Shuhua , Zhang Xuemei
Published: August 2022

Purpose: Lung adenocarcinoma is one of the most common malignancies. Though some historic breakthroughs have been made in lung adenocarcinoma, its molecular mechanisms of development remain elusive. The aim of this study was to identify the potential genes associated with the lung adenocarcinoma progression and to provide new ideas for the prognosis evaluation of lung adenocarcinoma.

Methods: The transcriptional profiles of ten pairs of snap-frozen tumor and adjacent normal lung tissues were obtained by performing RNA-seq. Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks in order to explore the associations of gene sets with the clinical features and to investigate the functional enrichment analysis of co-expression genes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Set Enrichment Analysis (GSEA) analyses were performed using clusterProfiler. The protein-protein network (PPI) was established using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and hub genes were identified using Cytohubba in Cytoscape. Transcription factor enrichment analysis was performed by the RcisTarget program in R language.

Results: Based on RNA-seq data, 1,545 differentially expressed genes (DEGs) were found. Eight co-expression modules were identified among these DEGs. The blue module exhibited a strong correlation with LUAD, in which ADCY4, RXFP1, AVPR2, CALCRL, ADRB1, RAMP3, RAMP2 and VIPR1 were hub genes. A low expression level of RXFP1, AVPR2, ADRB1 and VIPR1 was detrimental to the survival of LUAD patients. Genes in the blue module enriched in 86 Gene Ontology terms and five KEGG pathways. We also found that transcription factors EGR3 and EXOSC3 were related to the biological function of the blue module. Overall, this study brings a new perspective to the understanding of LUAD and provides possible molecular biomarkers for prognosis evaluation of LUAD.
Research Trends, Enabling Technologies and Application Areas for Big Data
Authors: Lundberg Lars , Grahn Håkan
Published: August 2022

The availability of large amounts of data in combination with Big Data analytics has transformed many application domains. In this paper, we provide insights into how the area has developed in the last decade. First, we identify seven major application areas and six groups of important enabling technologies for Big Data applications and systems. Then, using bibliometrics and an extensive literature review of more than 80 papers, we identify the most important research trends in these areas. In addition, our bibliometric analysis also includes trends in different geographical regions. Our results indicate that manufacturing and agriculture or forestry are the two application areas with the fastest growth. Furthermore, our bibliometric study shows that deep learning and edge or fog computing are the enabling technologies increasing the most. We believe that the data presented in this paper provide a good overview of the current research trends in Big Data and that this kind of information is very useful when setting strategic agendas for Big Data research.
Hyperspectral Estimates of Soil Moisture Content Incorporating Harmonic Indicators and Machine Learning
Authors: Jiang Xueqin , Luo Shanjun , Ye Qin , Li Xican , Jiao Weihua
Published: August 2022

Soil is one of the most significant natural resources in the world, and its health is closely related to food security, ecological security, and water security. It is the basic task of soil environmental quality assessment to monitor the temporal and spatial variation of soil properties scientifically and reasonably. Soil moisture content (SMC) is an important soil property, which plays an important role in agricultural practice, hydrological process, and ecological balance. In this paper, a hyperspectral SMC estimation method for mixed soil types was proposed combining some spectral processing technologies and principal component analysis (PCA). The original spectra were processed by wavelet packet transform (WPT), first-order differential (FOD), and harmonic decomposition (HD) successively, and then PCA dimensionality reduction was used to obtain two groups of characteristic variables: WPT-FOD-PCA (WFP) and WPT-FOD-HD-PCA (WFHP). On this basis, three regression models of principal component regression (PCR), partial least squares regression (PLSR), and back propagation (BP) neural network were applied to compare the SMC predictive ability of different parameters. Meanwhile, we also compared the results with the estimates of conventional spectral indices. The results indicate that the estimation results based on spectral indices have significant errors. Moreover, the BP models (WFP-BP and WFHP-BP) show more accurate results when the same variables are selected. For the same regression model, the choice of variables is more important. The three models based on WFHP (WFHP-PCR, WFHP-PLSR, and WFHP-BP) all show high accuracy and maintain good consistency in the prediction of high and low SMC values. The optimal model was determined to be WFHP-BP with an R2 of 0.932 and a prediction error below 2%. This study can provide information on farm entropy before planting crops on arable land as well as a technical reference for estimating SMC from hyperspectral images (satellite and UAV, etc.).
Maintaining the Quality of ‘Red Jonaprince’ Apples during Storage by 1-Methylcyclopropene Preharvest and Postharvest Treatment
Authors: Tomala Kazimierz , Guzek Dominika , Głąbska Dominika , Małachowska Maria , Widłak Łukasz , Krupa Tomasz , Gutkowska Krystyna
Published: August 2022

‘Red Jonaprince’ cultivar production is rapidly increasing; therefore, the interest in harvesting in various regions, as well as in improving and maintaining the quality of the apple, is also increasing. The aim of this study was to analyze the possibility of applying 1-MCP treatment in pre- and postharvest treatment for ‘Red Jonaprince’ apples for maintaining the quality of the apple after Ultra-Low Oxygen (ULO) storage. The studied apples were assessed in four groups: 0—control group without any specific treatment applied; group 1—1-MCP applied preharvest; group 2—1-MCP applied postharvest; group 3—1-MCP applied preharvest and postharvest. Measurements were conducted directly after 3, 5 and 6 months of ULO storage and additionally after 7 days of simulated distribution for each period of storage. The 24 groups obtained (four treatments × three storage periods × two simulated distribution periods) were evaluated for firmness, soluble solids content (SSC) and titratable acidity (TA). No statistically significant impact on the SSC of ‘Red Jonaprince’ for all groups was stated (p > 0.05), while for the firmness and TA values, there was a statistically significant impact of applied treatment (ULO storage period and simulated distribution) (p < 0.05). Longer ULO storage as well as longer simulated distribution were associated with decreasing firmness and decreasing TA of apples (p < 0.05). For samples subjected to 1-MCP treatment postharvest and those subjected to 1-MCP treatment preharvest and postharvest combined, the apples in the majority of analyses were protected against decreasing firmness and decreasing TA. It may be concluded that postharvest 1-MCP treatment applied for ‘Red Jonaprince’ apples allows maintaining its quality parameters, including firmness and TA of fruits. Taking this into account, our recommendation is that the postharvest 1-MCP treatment should be sufficient, in order to avoid unreasonable management of 1-MCP, which is not justified to be applied preharvest, or preharvest and postharvest combined, for ‘Red Jonaprince’ apples.
Estimation of Fusarium Head Blight Severity Based on Transfer Learning
Authors: Gao Chunfeng , Gong Zheng , Ji Xingjie , Dang Mengjia , He Qiang , Sun Heguang , Guo Wei
Published: August 2022

The recognition accuracy of traditional image recognition methods is heavily dependent on the design of complicated and tedious hand-crafted features. In view of the problems of poor accuracy and complicated feature extraction, this study presents a methodology for the estimation of the severity of wheat Fusarium head blight (FHB) with a small sample dataset based on transfer learning technology and convolutional neural networks (CNNs). Firstly, we utilized the potent feature learning and feature expression capabilities of CNNs to realize the automatic learning of FHB characteristics. Using transfer learning technology, VGG16, ResNet50, and MobileNetV1 models were pre-trained on the ImageNet. The knowledge was transferred to the estimation of FHB severity, and the fully connected (FC) layer of the models was modified. Secondly, acquiring the wheat images at the peak of the outbreak of FHB as the research object, after preprocessing for size filling on the wheat images, the image dataset was expanded with operations such as mirror flip, rotation transformation, and superimposed noise to improve the performance of the model and reduce the overfitting of models. Finally, under the Tensorflow deep learning framework, the VGG16, ResNet50, and MobileNetV1 models were subjected to transfer learning. The results showed that in the case of transfer learning and data augmentation, the ResNet50 model in Accuracy, Precision, Recall, and F1 score was better than the other two models, giving the highest accuracy of 98.42% and F1 score of 97.86%. The ResNet50 model had the highest recognition accuracy, providing technical support and reference for the accurate recognition of FHB.
Seroprevalence of Toxoplasmosis among Shelter-Housed Felines in a Philadelphia Suburb
Authors: Mitchell Danni J. , Reinhard Chelsea L. , Cole Stephen D. , Stefanovski Darko , Watson Brittany
Published: August 2022

Members of the Felidae family are the definitive host of the ubiquitous zoonotic parasite Toxoplasma gondii. Few studies have been conducted to determine the epidemiology of T. gondii in domestic felines within animal shelter populations. The goal of this study was to assess seroprevalence in a limited-admission shelter in the greater Philadelphia area. Serum samples were collected from cats at a shelter in Media, Pennsylvania during the summer of 2018 to assess the proportion of the population that was IgM or IgG seropositive for antibodies against T. gondii, using a commercially available ELISA. Out of the 84 cats that were sampled, 24 cats were seropositive, giving a population prevalence of 28.6%. Nine cats were seropositive for IgM, nine were seropositive for IgG, and six were seropositive for both IgM and IgG. Based on our data, we found that a large percentage of this population was seronegative. Although the sample size in this study was limited and prevented us from obtaining statistically significant results, this research can serve as a pilot study for further investigations into the seroprevalence of toxoplasmosis among shelter-housed felines.
High Seroprevalence against SARS-CoV-2 among Dogs and Cats, Poland, 2021/2022
Authors: Kaczorek-Łukowska Edyta , Wernike Kerstin , Beer Martin , Wróbel Małgorzata , Małaczewska Joanna , Mikulska-Skupień Elżbieta , Malewska Karolina , Mielczarska Izabela , Siwicki Andrzej Krzysztof
Published: August 2022

The coronavirus SARS-CoV-2 is responsible for a pandemic in the human population that has unfolded since the beginning of 2020 and has led to millions of deaths globally. Apart from humans, SARS-CoV-2 has been confirmed in various animal species, including felines, canines, mustelids, and primates. Of these species, dogs and cats are the most popular companion animals worldwide. Several seroprevalence studies have already been performed in these animal species; however, the results vary depending on the location and especially the time of sampling. Here, serum samples were collected from a total of 388 dogs and 243 cats from three veterinary clinics in two cities (Gdańsk and Olsztyn) in Poland between October 2021 and February 2022, when the country was in the midst of the fourth wave of viral spread. All sera were tested for antibodies against SARS-CoV-2 by a multispecies ELISA based on the receptor-binding domain and by an indirect immunofluorescence assay (iIFA). Overall, 18.9% of the feline sera and 16.0% of the canine sera tested positive using ELISA and iIFA. This relatively high seroprevalence among randomly selected animals is most likely related to the high case numbers in the human population and indicates a continuous occurrence of transspecies virus transmissions from infected owners to their pets. Hence, dogs and cats should be included in monitoring studies and/or outbreak investigations for a better understanding of the epidemiology of this virus.
Correction: Wiśniewska et al. Heterospecific Fear and Avoidance Behaviour in Domestic Horses (Equus caballus). Animals 2021, 11, 3081
Authors: Wiśniewska Anna , Janczarek Iwona , Wilk Izabela , Tkaczyk Ewelina , Mierzicka Martyna , Stanley Christina R. , Górecka-Bruzda Aleksandra
Published: August 2022

The authors wish to make the following correction to this paper [...]
Glaucoma through Animal’s Eyes: Insights from the Evolution of Intraocular Pressure in Mammals and Birds
Authors: Hongjamrassilp Watcharapong , Zhang Roger , Natterson-Horowitz B. , Blumstein Daniel T.
Published: August 2022

Glaucoma, an eye disorder caused by elevated intraocular pressure (IOP), is the leading cause of irreversible blindness in humans. Understanding how IOP levels have evolved across animal species could shed light on the nature of human vulnerability to glaucoma. Here, we studied the evolution of IOP in mammals and birds and explored its life history correlates. We conducted a systematic review, to create a dataset of species-specific IOP levels and reconstructed the ancestral states of IOP using three models of evolution (Brownian, Early burst, and Ornstein–Uhlenbeck (OU)) to understand the evolution of glaucoma. Furthermore, we tested the association between life history traits (e.g., body mass, blood pressure, diet, longevity, and habitat) and IOP using phylogenetic generalized least squares (PGLS). IOP in mammals and birds evolved under the OU model, suggesting stabilizing selection toward an optimal value. Larger mammals had higher IOPs and aquatic birds had higher IOPs; no other measured life history traits, the type of tonometer used, or whether the animal was sedated when measuring IOP explained the significant variation in IOP in this dataset. Elevated IOP, which could result from physiological and anatomical processes, evolved multiple times in mammals and birds. However, we do not understand how species with high IOP avoid glaucoma. While we found very few associations between life history traits and IOP, we suggest that more detailed studies may help identify mechanisms by which IOP is decoupled from glaucoma. Importantly, species with higher IOPs (cetaceans, pinnipeds, and rhinoceros) could be good model systems for studying glaucoma-resistant adaptations.
Effects of Polymeric Media-Coated Gynosaponin on Microbial Abundance, Rumen Fermentation Properties and Methanogenesis in Xinjiang Goats
Authors: Li Peng , Mehmood Irum Mohd , Chen Wei
Published: August 2022

Gynosaponin is known to modulate rumen methanogenesis and microbial fermentation characteristics in ruminants. The current experiment aimed to determine the time-dependent effects of intraruminal polymeric media-coated gynosaponin (PMCG) supplementation on the methanogenesis, rumen fermentation properties and microbial abundance in Xinjiang goats. Eight goats were used in a 2 × 2 crossover arrangement with a PMCG group (8 g/kg DMI) and a control group (0 g/kg DMI). The experiment was divided into four phases, each lasted 21 d. Ruminal contents were obtained for analysis of rumen fermentation properties and microbial abundance. Protozoa numbers were counted by microscope and the abundance of methanogens, rumen fungi and cellulolytic bacteria were quantified by real-time PCR. The results indicated that PMCG significantly reduced methane production (p < 0.05) during the first two phases but this increased to baseline again during the last two phases. Meanwhile, the concentration of acetate decreased remarkably, which resulted in a significant reduction in the acetate to propionate ratio and total VFA concentration (p < 0.05). However, other rumen properties and dry matter intake were not affected (p > 0.05). During the first and second phases, the protozoa numbers and gene copies of methanogens, total bacteria and F. succinogens relative to the 16 s rDNA were all slightly decreased, but the statistical results were not significant. However, the ruminal supplementation of PMCG had little effect on other tested microbes. Accordingly, it was concluded that the addition of PMCG had an inhibitory effect on methane production probably due to a decline in methanogen numbers.
Therapeutic Options and Outcomes for the Treatment of Neonates and Preterms with Gram-Negative Multidrug-Resistant Bacteria: A Systematic Review
Authors: Chiusaroli Lorenzo , Liberati Cecilia , Caseti Maria , Rulli Luigi , Barbieri Elisa , Giaquinto Carlo , Donà Daniele
Published: August 2022

(1) Background: Infections caused by multidrug-resistant (MDR) or extensively drug-resistant (XDR) bacteria represent a challenge in the neonatal population due to disease severity and limited therapeutic possibilities compared to adults. The spread of antimicrobial resistance and drug availability differ significantly worldwide. The incidence of MDR bacteria has constantly risen, causing an increase in morbidity, mortality, and healthcare costs in both high-income (HIC) and low- and middle-income countries (LMIC). Therefore, more evidence is needed to define the possible use of newer molecules and to optimize combination regimens for the oldest antimicrobials in neonates. This systematic review aims to identify and critically appraise the current antimicrobial treatment options and the relative outcomes for MDR and XDR Gram-negative bacterial infections in the neonatal population. (2) Methods: A literature search for the treatment of MDR Gram-negative bacterial infections in neonates (term and preterm) was conducted in Embase, MEDLINE, and Cochrane Library. Studies reporting data on single-patient-level outcomes related to a specific antibiotic treatment for MDR Gram-negative bacterial infection in children were included. Studies reporting data from adults and children were included if single-neonate-level information could be identified. We focused our research on four MDROs: Enterobacterales producing extended-spectrum beta-lactamase (ESBL) or carbapenemase (CRE), Pseudomonas aeruginosa, and Acinetobacter baumannii. PROSPERO registration: CRD42022346739 (3) Results: The search identified 11,740 studies (since January 2000), of which 22 fulfilled both the inclusion and exclusion criteria and were included in the analysis. Twenty of these studies were conducted in LMIC. Colistin is the main studied and used molecule to treat Gram-negative MDR bacteria for neonate patients in the last two decades, especially in LMIC, with variable evidence of efficacy. Carbapenems are still the leading antibiotics for ESBL Enterobacterales, while newer molecules (i.e., beta-lactam agents/beta-lactamase inhibitor combination) are promising across all analyzed categories, but data are few and limited to HICs. (4) Conclusions: Data about the treatment of Gram-negative MDR bacteria in the neonatal population are heterogeneous and limited mainly to older antimicrobials. Newer drugs are promising but not affordable yet for many LMICs. Therefore, strategies cannot be generalized but will differ according to the country’s epidemiology and resources. More extensive studies are needed to include new antimicrobials and optimize the combination strategies for the older ones.
Human Placental Extract Delays In Vitro Cellular Senescence through the Activation of NRF2-Mediated Antioxidant Pathway
Authors: Huang Liguo , Chin Lit-Chein , Kimura Koichi , Nakahata Yasukazu
Published: August 2022

Senescent cells accumulate in the organs of aged animals and exacerbate organ dysfunction, resulting in age-related diseases. Oxidative stress accelerates cellular senescence. Placental extract, used in the alleviation of menopausal symptoms and promotion of wound healing and liver regeneration, reportedly protects against oxidative stress. In this study, we investigated the effects of human placental extract (HPE) on cellular senescence in normal human dermal fibroblasts (NHDFs) under oxidative stress conditions. We demonstrated that HPE delays the onset of cellular senescence. Next-generation sequencing analysis revealed that under oxidative stress conditions, HPE treatment enhanced the expression of the antioxidant genes CYGB, APOE, NQO1, and PTGS1. Further, HPE treatment under oxidative stress conditions increased the protein level of nuclear factor-erythroid factor 2-related factor 2 (NRF2)—a vital molecule in the antioxidant pathway—via post-transcriptional and/or post-translational regulations. These findings indicate that HPE treatment in NHDFs, under chronic oxidative stress, delays cellular senescence by mitigating oxidative stress via upregulation of the NRF2-mediated antioxidant pathway, and HPE treatment could potentially ameliorate skin-aging-associated damage, in vivo.
Supplementation of Dimethylglycine Sodium Salt in Sow Milk Reverses Skeletal Muscle Redox Status Imbalance and Mitochondrial Dysfunction of Intrauterine Growth Restriction Newborns
Authors: Bai Kaiwen , Jiang Luyi , Li Qiming , Zhang Jingfei , Zhang Lili , Wang Tian
Published: August 2022

The current study sought to understand the mechanism underlying skeletal muscle dysfunction brought on by intrauterine growth restriction (IUGR) and to explore the treatment benefits of applying dimethylglycine sodium salt (DMG-Na) in sow milk to newborns during the suckling period. Each of the 10 sows delivered one newborn with a normal birth weight (NBW) and one with an IUGR. Additionally, two NBW and two IUGR newborns were collected per litter of another 10 sows. The 20 NBW newborns were divided between the N (sow milk) and ND (sow milk + 0.1% DMG-Na) groups, while 20 IUGR newborns were divided between the I (sow milk) and ID (sow milk + 0.1% DMG-Na) groups. The skeletal muscle histomorphology, redox status, and levels of gene and protein expression were worse (p < 0.05) in the I group than in the N group. In addition, supplementation with DMG-Na (ND and ID groups) improved (p < 0.05) those parameters compared to the unsupplemented groups (N and I groups). Inhibited nuclear factor erythroid 2-related factor 2 (Nrf2)/sirtuin 1 (SIRT1)/peroxisome proliferator-activated receptorγcoactivator-1α (PGC-1α) activity resulted in decreased redox status, skeletal muscle structural damage, skeletal muscle mitochondrial function impairment, and decreased performance in IUGR newborns. Supplementation of DMG-Na in sow milk activated the Nrf2/SIRT1/PGC-1α in IUGR newborns, thereby improving their skeletal muscle performance.
Statistical and Comparative Analysis of Multi-Channel Infrared Anomalies before Earthquakes in China and the Surrounding Area
Authors: Yue Yingbo , Chen Fuchun , Chen Guilin
Published: August 2022

Abundant infrared remote sensing images and advanced information processing technologies are used to predict earthquakes. However, most studies only use single long-wave infrared data or its products, and the accuracy of prediction is not high enough. To solve this problem, this paper proposes a statistical method based on connected domain recognition to analyze multi-channel anomalies. We extract pre-seismic anomalies from multi-channel infrared remote sensing images using the relative power spectrum, then calculate positive predictive values, true positive rates and probability gains in different channels. The results show that the probability gain of the single-channel prediction method is extremely low. The positive predictive value of four-channel anomalies is 41.94%, which is higher than that of single-channel anomalies with the same distance threshold of 200 km. The probability gain of the multi-channel method is 2.38, while that of the single-channel method using the data of any channel is no more than 1.26. This study shows the advantages of the multi-channel method to predict earthquakes and indicates that it is feasible to use multi-channel infrared remote sensing images to improve the accuracy of earthquake prediction.
The Sky-Status Climatology of Greece: Emphasis on Sunshine Duration and Atmospheric Scattering
Authors: Kambezidis Harry D.
Published: August 2022

The aim of this work is the study of the sky conditions climatology over Greece based on the diffuse-fraction (kd) limits, for clear, kd ∈ [0, 0.26]; intermediate, kd ∈ (0.26, 0.78); and overcast, kd ∈ (0.78, 1) skies. kd is, therefore, used here to characterise the sky conditions over a site. Its values are estimated from diffuse and global horizontal solar irradiances the typical meteorological years of 43 selected Greek sites. The kd values in each specific range are equivalent to sunshine durations (SSDs) under the particular sky conditions. Annual, seasonal, and intra-annual variations in SSDs are estimated with regression equations to fit their means. Clear skies comprise 33%, intermediate 40%, and overcast 27% of the time in a year. kd, as an atmospheric scattering index (ASI), shows dependence on the sites’ geographical latitude: best-fit lines mean ASIs are derived showing no trend, while overcast skies show a slight negative trend. A comparison of the clear-sky SSDs for four Greek sites from the Global Climate Data and one site from the Academy of Sciences of Moldova with those derived from kd shows a remarkable difference. A new methodology is developed that results in much smaller differences. Finally, maps of the annual SSDs and ASIs are derived for Greece.
A Resonant Ring Topology Approach to Power Line Communication Systems within Photovoltaic Plants
Authors: Morales-Aragonés José Ignacio , Williams Matthew St. Michael , Gómez Víctor Alonso , Gallardo-Saavedra Sara , Redondo-Plaza Alberto , Fernández-Martínez Diego , Sánchez-Pacheco Francisco José , Cuadro Juan Gabriel Fajardo , Hernández-Callejo Luis
Published: August 2022

Within this study, single-cable propagation facilitated by PV strings’ wiring characteristics is considered for an adapted design of PLC electronics. We propose to close the communications signal path, resulting in a ring topology where a resonance condition could be implemented. A PLC topology using the resulting circular closed-loop path of a PV series string as its physical communication support is designed and leveraged for practical use. When the path length or the number of transceivers is changed, the resonance properties that come with the circular path as the physical support are affected but are shown to be preserved with the application of automatic adjustable tuning. This automatic tuning guarantees that the resonance improves propagation parameters and reverts the system to its optimal values at the chosen carrier frequency.
A Sentence Prediction Approach Incorporating Trial Logic Based on Abductive Learning
Authors: Ouyang Long , Huang Ruizhang , Chen Yanping , Qin Yongbin
Published: August 2022

Sentencing prediction is an important direction of artificial intelligence applied to the judicial field. The purpose is to predict the trial sentence for the case based on the description of the case in the adjudication documents. Traditional methods mainly use neural networks exclusively, which are trained on a large amount of data to encode textual information and then directly regress or classify out the sentence. This shows that machine learning methods are effective, but are extremely dependent on the amount of data. We found that there is still external knowledge such as laws and regulations that are not used. Moreover, the prediction of sentences in these methods does not fit well with the trial process. Thus, we propose a sentence prediction method that incorporates trial logic based on abductive learning, called SPITL. The logic of the trial is reflected in two aspects: one is that the process of sentence prediction is more in line with the logic of the trial, and the other is that external knowledge, such as legal texts, is utilized in the process of sentence prediction. Specifically, we establish a legal knowledge base for the characteristics of theft cases, translating relevant laws and legal interpretations into first-order logic. At the same time, we designed the process of sentence prediction according to the trial process by dividing it into key circumstance element identification and sentence calculation. We fused the legal knowledge base as weakly supervised information into a neural network through the combination of logical inference and machine learning. Furthermore, a sentencing calculation method that is more consistent with the sentencing rules is proposed with reference to the Sentencing Guidelines. Under the condition of the same training data, the effect of this model in the experiment of responding to the legal documents of theft cases was improved compared with state-of-the-art models without domain knowledge. The results are not only more accurate as a sentencing aid in the judicial trial process, but also more explanatory.
A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection
Authors: Qazi Emad Ul Haq , Almorjan Abdulrazaq , Zia Tanveer
Published: August 2022

The connectivity of devices through the internet plays a remarkable role in our daily lives. Many network-based applications are utilized in different domains, e.g., health care, smart environments, and businesses. These applications offer a wide range of services and provide services to large groups. Therefore, the safety of network-based applications has always been an area of research interest for academia and industry alike. The evolution of deep learning has enabled us to explore new areas of research. Hackers make use of the vulnerabilities in networks and attempt to gain access to confidential systems and information. This information and access to systems can be very harmful and portray losses beyond comprehension. Therefore, detection of these network intrusions is of the utmost importance. Deep learning based techniques require minimal inputs while exploring every possible feature set in the network. Thus, in this paper, we present a one-dimensional convolutional neural network-based deep learning architecture for the detection of network intrusions. In this research, we detect four different types of network intrusions, i.e., DoS Hulk, DDoS, and DoS Goldeneye which belong to the active attack category, and PortScan, which falls in the passive attack category. For this purpose, we used the benchmark CICIDS2017 dataset for conducting the experiments and achieved an accuracy of 98.96% as demonstrated in the experimental results.
Experimental and Numerical Studies on Ground Shock Generated by Large Equivalent Surface Explosions
Authors: Xia Chenxi , Chen Li , Xu Rongzheng , Cao Mingjin , Chen Dapeng , Fang Qin
Published: August 2022

Lately, explosions with a large TNT equivalent have occurred with alarming frequency causing severe structural damage. The damage suffered by these structures has been exacerbated by the ground shock generated during these large equivalent explosions. The aim of this work is to study the ground-shock propagation behaviors, the areas affected by them, and determine the minimum safe distance for various structures. To measure ground shock data at different distances from the epicenter of the blast, actual 1 t and 10 t TNT surface explosion experiments were performed. The velocity and attenuation coefficient of the ground shock generated by the 1 t TNT surface explosion were determined, and the empirical equations provided by the UFC 3-340-01 standard were validated. Additionally, numerical analyses were performed to analyze the effects of ground shocks on buildings around an explosion. The maximum particle vibration velocities and attenuation behaviors of a 10 t TNT surface explosion as well as the minimum safe distances for a variety of structures were obtained from the numerical analyses.
Effect of Disperse Red 1 Azobenzene Dye Doping and Annealing on the Thermomechanical and Photomechanical Properties of PMMA Fibers
Authors: Ghorbanishiadeh Zoya , Zhou Bojun , Sheibani Karkhaneh Morteza , Oehler Rebecca , Kuzyk Mark
Published: August 2022

This work studies the effect of azobenzene dye Disperse Red 1 (DR1) doping and annealing on the thermomechanical and photomechanical properties of poly(methyl methacrylate) (PMMA) fibers. The mechanical properties are measured as a function of temperature, pump light intensity, and polarization. We find that doping with DR1 increases the stiffness and the glass transition temperature (Tg) of the PMMA fibers. Moreover, annealing below Tg decreases Young’s modulus and increases Tg. Finally, the photothermal heating contribution to the photomechanical response and the length change during laser exposure are determined in both unannealed and annealed plain PMMA and DR1-doped PMMA fibers. We find that photothermal heating is the dominant mechanism and the effect of photoisomerization is negligible. The temperature-dependent photomechanical efficiencies are also determined.
Assessment of Escape Safety of Cruise Ships Based on Dislocation Accumulation and Social Force Models
Authors: Li Jianing , Wang Gaoshuai , Guo Yong , Liu Chao , Huang Yiming , Chen Gang
Published: August 2022

The escape safety of passengers is the primary design requirement of cruise ships. However, larger and more complex structural schemes make the existing escape safety assessment methods insufficient to accurately calculate the safety index of the passengers’ escape. Therefore, this paper proposes a novel assessment method to infer the passengers’ escape safety index of large cruise ships. Firstly, on the basis of quantifying personnel attributes and moving speed, a confluence algorithm based on the dislocation accumulation model is proposed to correct the flow model of passenger escape. Subsequently, a passenger escape flow method based on the social force model is established. The proposed method is applied to the escape safety assessment of a 130,000-ton cruise ship. The validation of the results is conducted by a comparative analysis between the proposed method and the traditional method and the results of simulation tools. The comparison confirmed that the proposed method has merits in computing accurate results. Overall, the proposed method supports the safety design of cruise ships.
Enhanced Seagull Optimization with Natural Language Processing Based Hate Speech Detection and Classification
Authors: Asiri Yousef , Halawani Hanan T. , Alghamdi Hanan M. , Abdalaha Hamza Saadia Hassan , Abdel-Khalek Sayed , Mansour Romany F.
Published: August 2022

Hate speech has become a hot research topic in the area of natural language processing (NLP) due to the tremendous increase in the usage of social media platforms like Instagram, Twitter, Facebook, etc. The facelessness and flexibility provided through the Internet have made it easier for people to interact aggressively. Furthermore, the massive quantity of increasing hate speech on social media with heterogeneous sources makes it a challenging task. With this motivation, this study presents an Enhanced Seagull Optimization with Natural Language Processing Based Hate Speech Detection and Classification (ESGONLP-HSC) model. The major intention of the presented ESGONLP-HSC model is to identify and classify the occurrence of hate speech on social media websites. To accomplish this, the presented ESGONLP-HSC model involves data pre-processing at several stages, such as tokenization, vectorization, etc. Additionally, the Glove technique is applied for the feature extraction process. In addition, an attention-based bidirectional long short-term memory (ABLSTM) model is utilized for the classification of social media text into three classes such as neutral, offensive, and hate language. Moreover, the ESGO algorithm is utilized as a hyperparameter optimizer to adjust the hyperparameters related to the ABLSTM model, which shows the novelty of the work. The experimental validation of the ESGONLP-HSC model is carried out, and the results are examined under diverse aspects. The experimentation outcomes reported the promising performance of the ESGONLP-HSC model over recent state of art approaches.