bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Exogenous Glucocorticoid Infusion on Appetitic Center Development in Postnatal Dairy Bull Calves
Authors: McCarty Keelee J. , Pratt Scott L. , Long Nathan M.
Published: June 2023

The objective of this study was to determine the effects of exogenous glucocorticoid administration on leptin concentrations and brain development markers, such as protein and hypothalamic gene expression, in dairy bull calves. Within 4 h of parturition, Holstein bulls were intravenously infused with either a low cortisol dose (LC; n = 9, 3.5 µg/kg of body weight (BW)), high cortisol dose (HC; n = 9, 7.0 µg/kg BW), or control (CON; n = 9, saline) dose, with a 2nd infusion 24 h postpartum. Jugular blood was collected prior to infusion and daily until the calves were euthanized (day 5). Cerebrospinal fluid (CSF) from the third ventricle and adipose (omental, perirenal, and mesenteric) and hypothalamic tissue were collected. The blood and CSF samples were analyzed for leptin concentrations. The data were analyzed using SAS. Serum (p = 0.013) and CSF (p = 0.005) leptin concentrations in HC- and LC-treated calves were decreased compared with CON-treated calves. Leptin protein expression was decreased (p < 0.044) in perirenal and omental adipose tissue of LC-treated calves compared with CON-treated calves. Gene abundance of brain-derived neurotrophic factor and fibroblast growth factors 1 and 2 were decreased (p < 0.006) in HC- and LC-treated calves compared with CON-treated calves. In summary, cortisol administered to dairy bull calves reduced leptin concentrations, decreased leptin protein expression in perirenal and omental adipose tissue, and altered gene expression in hypothalamic tissue.
Polymorphism at the CSN1S1 Locus and Energy Intake Level Affect Milk Traits and Casein Profiles in Rossa Mediterranea Goats
Authors: Tumino Serena , Di Trana Adriana , Valenti Bernardo , Bordonaro Salvatore , Claps Salvatore , Avondo Marcella , Di Gregorio Paola
Published: June 2023

A total of twenty-seven Rossa Mediterranea lactating goats, consisting of nine homozygous for strong alleles (AA), twelve heterozygous (AF) and six homozygous for weak alleles (FF) at the CSN1S1 locus, were used to evaluate the effect of genotype, diet and genotype × diet interaction on goat milk traits and casein profile. The goats were used in a 3 × 3 factorial arrangement of treatments, with three genotypes (AA, AF and FF) and three different energy intake levels: high (H), medium (M) and low (L). The diets supplied a complete pelleted feed containing 65% of alfalfa hay, respectively, at 150%, 100% and 70% of the total energy requirements. Milk yield was significantly affected by the genotype and diet: Lower levels were found in FF goats than in AA and AF genotypes (673.7 vs. 934.5 and 879.8 d/g, respectively; p = 0.002) as well as in goats fed with the L diet (651.5 vs. 1041 and 852.9 g/d for H and M diet, respectively, p < 0.001). The genotype influenced the casein profile. Specifically, AA goat milk exhibited higher concentrations of total casein and αs1-casein compared to AF and FF genotypes (for total casein and αs1-casein, respectively: 24.9 vs. 20.4 and 19.8 g/kg, p = 0.001; 7.2 vs. 3.7 and 0.7 g/kg, p < 0.001), while the FF genotype showed higher values for αs2-casein concentrations compared to homozygous AA and heterozygous AF goats (3.1 vs. 2.4 and 2.5 g/kg, respectively, p < 0.001). A significant genotype x diet interaction occurred for αs2-casein levels (g/kg) (p = 0.034) and αs1-casein yields (p = 0.027): The αs2-casein level was not affected by the diet in AA goats, whereas it increased with energy intake in AF and FF genotypes. Conversely, the αs1-casein yield gradually increased with energy intake in AA and AF groups, whereas the diet in FF goats did not modify it. The results demonstrated that high energy input, as well as the strong allele at the CSN1S1 locus, enhanced milk production and casein concentrations. Furthermore, they confirmed the existence of an interaction between αs1-casein polymorphism and diets, influencing the milk casein composition and yield.
FeTPPS, a Peroxynitrite Decomposition Catalyst, Ameliorates Nitrosative Stress in Human Spermatozoa
Authors: Uribe Pamela , Barra Javiera , Painen Kevin , Zambrano Fabiola , Schulz Mabel , Moya Claudia , Isachenko Vladimir , Isachenko Evgenia , Mallmann Peter , Sánchez Raúl
Published: June 2023

Excessive levels of reactive nitrogen species (RNS), such as peroxynitrite, promote nitrosative stress, which is an important cause of impaired sperm function. The metalloporphyrin FeTPPS is highly effective in catalyzing the decomposition of peroxynitrite, reducing its toxic effects in vivo and in vitro. FeTPPS has significant therapeutic potential in peroxynitrite-related diseases; however, its effects on human spermatozoa under nitrosative stress have not been described. This work aimed to evaluate the in vitro effect of FeTPPS against peroxynitrite-mediated nitrosative stress in human spermatozoa. For this purpose, spermatozoa from normozoospermic donors were exposed to 3-morpholinosydnonimine, a molecule that generates peroxynitrite. First, the FeTPPS-mediated peroxynitrite decomposition catalysis was analyzed. Then, its individual effect on sperm quality parameters was evaluated. Finally, the effect of FeTPPS on ATP levels, motility, mitochondrial membrane potential, thiol oxidation, viability, and DNA fragmentation was analyzed in spermatozoa under nitrosative stress conditions. The results showed that FeTPPS effectively catalyzes the decomposition of peroxynitrite without affecting sperm viability at concentrations up to 50 μmol/L. Furthermore, FeTPPS mitigates the deleterious effects of nitrosative stress on all sperm parameters analyzed. These results highlight the therapeutic potential of FeTPPS in reducing the negative impact of nitrosative stress in semen samples with high RNS levels.
Zero-Shot Image Classification with Rectified Embedding Vectors Using a Caption Generator
Authors: Hur Chan , Park Hyeyoung
Published: June 2023

Although image recognition technologies are developing rapidly with deep learning, conventional recognition models trained by supervised learning with class labels do not work well when test inputs from untrained classes are given. For example, a recognizer trained to classify Asian bird species cannot recognize the species of kiwi, because the class label “kiwi” and its image samples have not been seen during training. To overcome this limitation, zero-shot classification has been studied recently, and the joint-embedding-based approach has been suggested as one of the promised solutions. In this approach, image features and text descriptions belonging to the same class are trained to be closely located in a common joint-embedding space. Once we obtain the embedding function that can represent the semantic relationship of image–text pairs in training data, test images and text descriptions (prototypes) of unseen classes can also be mapped to the joint-embedding space for classification. The main challenge with this approach is mapping inputs of two different modalities into a common space, and previous works suffer from the inconsistency between the distribution of two feature sets on joint-embedding space extracted from the heterogeneous inputs. To treat this problem, we propose a novel method of employing additional textual information to rectify the visual representation of input images. Since the conceptual information of test classes is generally given as texts, we expect that the additional descriptions from a caption generator can adjust the visual feature for better matching with the representation of the test classes. We also propose to use the generated textual descriptions to augment training samples for learning joint-embedding space. In the experiments on two benchmark datasets, the proposed method shows significant performance improvements of 1.4% on the CUB dataset and 5.5% on the flower dataset, in comparison to existing models.
University Student Dropout Prediction Using Pretrained Language Models
Authors: Won Hyun-Sik , Kim Min-Ji , Kim Dohyun , Kim Hee-Soo , Kim Kang-Min
Published: June 2023

Predicting student dropout from universities is an imperative but challenging task. Numerous data-driven approaches that utilize both student demographic information (e.g., gender, nationality, and high school graduation year) and academic information (e.g., GPA, participation in activities, and course evaluations) have shown meaningful results. Recently, pretrained language models have achieved very successful results in understanding the tasks associated with structured data as well as textual data. In this paper, we propose a novel student dropout prediction framework based on demographic and academic information, using a pretrained language model to capture the relationship between different forms of information. To this end, we first formulate both types of information in natural language form. We then recast the student dropout prediction task as a natural language inference (NLI) task. Finally, we fine-tune the pretrained language models to predict student dropout. In particular, we further enhance the model using a continuous hypothesis. The experimental results demonstrate that the proposed model is effective for the freshmen dropout prediction task. The proposed method exhibits significant improvements of as much as 9.00% in terms of F1-score compared with state-of-the-art techniques.
Arabic News Classification Based on the Country of Origin Using Machine Learning and Deep Learning Techniques
Authors: Zamzami Nuha , Himdi Hanen , Sabbeh Sahar F.
Published: June 2023

With the rise of Arabic news articles published daily, people are becoming increasingly concerned about following the news from reliable sources, especially regarding events that impact their country. To assess a news article’s significance to the user, it is essential to identify the article’s country of origin. This paper proposes several classification models that categorize Arabic news articles based on their country of origin. The models were developed using comprehensive machine learning and deep learning techniques with several feature training methods. The results show the ability of our model to classify news articles based on their country of origin, with close accuracy between machine learning and deep learning techniques of up to 94%.
Comparative Study of Plantago media Extracts in the Treatment of Acanthamoeba sp. Trophozoites
Authors: Budzianowska Anna , Derda Monika , Budzianowski Jaromir , Szopa Agnieszka , Kikowska Małgorzata
Published: June 2023

(1) Background: The aim of the study was to compare the potency of Plantago media L. (Plantaginaceae) extracts on Acanthamoeba sp. trophozoites, which are opportunistic protozoan parasites leading to several dangerous diseases; (2) Methods: The chromatographically (TLC, HPLC-DAD) characterized water fractions of the extracts from biomass from in vitro cultures (shoots and roots), leaves, and inflorescences from field cultivation were used for the study of the acanthamoebic activity in a Thoma haemocytometer chamber; (3) Results: The anti-amoebic effect at the lowest concentration (1.0 mg/mL) was demonstrated only by the extract of the leaves from the cultivation (50.50% inhibition). The remaining samples inhibited the growth of parasites from a concentration of 5.0 mg/mL in the range of 41.36% inflorescences to 63.89% shoots in vitro. Quantitative determinations of phenolic compounds in the tested extracts indicate a tendency to increase the potency of the anti-amoebic effect with the content of a phenylethanoid glycoside—acteoside. The maximum content of this compound was determined in leaves from field cultivation (6.64%) and the minimum in inflorescences (0.65%). This is confirmed by the range of the lowest IC50 values (the strongest biological activity) for the tested samples, 0.95–1.80 mg/mL for leaves from cultivation, and the high values, 9.70–5.30 mg/mL for inflorescences and in-vitro-derived roots. The strength of the biological activity of the extracts correlated with the content of acteoside, which constituted 84–93% of the sum of phenolic compounds determined; (4) Conclusions: The performed investigations proved the anti-acanthamoebic efficacy of Plantago media organs, including those obtainable by biotechnological methods, and indicated phenylethanoid glycosides, their main phenolic constituents, to be responsible for the activity. To our knowledge, this is the first report on the amoebicidal activity of Plantago media extracts from biomass produced by biotechnological methods and organs of an intact plant.
Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges
Authors: Aldoseri Abdulaziz , Al-Khalifa Khalifa N. , Hamouda Abdel Magid
Published: June 2023

The use of artificial intelligence (AI) is becoming more prevalent across industries such as healthcare, finance, and transportation. Artificial intelligence is based on the analysis of large datasets and requires a continuous supply of high-quality data. However, using data for AI is not without challenges. This paper comprehensively reviews and critically examines the challenges of using data for AI, including data quality, data volume, privacy and security, bias and fairness, interpretability and explainability, ethical concerns, and technical expertise and skills. This paper examines these challenges in detail and offers recommendations on how companies and organizations can address them. By understanding and addressing these challenges, organizations can harness the power of AI to make smarter decisions and gain competitive advantage in the digital age. It is expected, since this review article provides and discusses various strategies for data challenges for AI over the last decade, that it will be very helpful to the scientific research community to create new and novel ideas to rethink our approaches to data strategies for AI.
Integrated Earthquake Catalog II: The Western Sector of the Russian Arctic
Authors: Vorobieva Inessa A. , Gvishiani Alexei D. , Shebalin Peter N. , Dzeboev Boris A. , Dzeranov Boris V. , Skorkina Anna A. , Sergeeva Natalia A. , Fomenko Natalia A.
Published: June 2023

The article is a continuation of the research on creating the most complete and representative earthquake catalogs by combining all available data from regional, national, and international seismological agencies and reducing magnitudes to a uniform scale. The task of identifying and removing duplicates that arise during the merging process is solved using the authors’ modification of the nearest neighbor method. It is evident that the intelligent merging of different earthquake catalogs for the same territory will improve the completeness and representativeness of events in the final integrated catalog. In this article, the earthquake catalog of the western sector of the Arctic zone of the Russian Federation (AZRF) covering the period 1962–2022 was created by merging three regional Russian catalogs and the ISC catalog. The ratio of magnitude types in the catalog for different seismic networks was analyzed, and magnitude estimates were unified based on the obtained ratios. For analyzing seismic activity in the western AZRF, it is recommended to use earthquakes from the period 1998–2020 when the catalog was significantly cleaned from explosions and other events of the “non-earthquake” type.
A Multivariate Machine Learning Model of Adsorptive Lindane Removal from Contaminated Water
Authors: Akinpelu Adeola Akeem , Nazal Mazen K. , Shafiullah Md , Islam Md Kamrul , Islam Mohammed Monirul , Rahman Aminur , Rahman Syed Masiur , Rahman Muhammad Muhitur
Published: June 2023

It is challenging to use conventional one-variable-at-time (OVAT) batch experiments to evaluate multivariate/inter-parametric interactions between physico-chemical variables that contribute to the adsorptive removal of contaminants. Thus, chemometric prediction approaches for multivariate calibration and analysis reveal the impact of multi-parametric variation on the process of concern. Hence, we aim to develop an artificial neural network (ANN), and stepwise regression (SR) models for multivariate calibration and analysis utilizing OVAT data prepared through experimentation. After comparing the models’ performance, ANN was the superior model for this application in our work. The standard deviations (SD) between the observed and ANN-predicted values were very close. The average correlation coefficient (R2) between observed and ANN-predicted values for the training dataset was 96.9%. This confirms the ability of our developed ANN model to forecast lindane removal accurately. The testing dataset correlation coefficients (89.9% for ANN and 67.75% for SR) demonstrated a better correlation between observed and predicted ANN values. The ANN model training and testing dataset RMSE values were 1.482 and 2.402, lower than the SR values of 4.035 and 3.890. The MAPE values for the ANN model’s training and testing datasets, 0.018 and 0.031, were lower than those for the SR model. The training and testing datasets have low RSR and PBIAS values, implying model strength. The R2 and WIA values are above 0.90 for both datasets, proving the ANN model’s accuracy. Applying our developed ANN model will reduce the cost of removing inorganic and organic impurities, including lindane, and optimize chemical utilization.
Efficacy of Carbamide and Hydrogen Peroxide Tooth Bleaching Techniques in Orthodontic and Restorative Dentistry Patients: A Scoping Review
Authors: Malcangi Giuseppina , Patano Assunta , Inchingolo Alessio Danilo , Ciocia Anna Maria , Piras Fabio , Latini Giulia , Di Pede Chiara , Palmieri Giulia , Laudadio Claudia , Settanni Vito , Garofoli Grazia , de Ruvo Elisabetta , Buongiorno Silvio , Bordea Ioana Roxana , Xhajanka Edit , Di Venere Daniela , Inchingolo Francesco , Dipalma Gianna , Inchingolo Angelo Michele
Published: June 2023

Background: Dental aesthetic procedures aim to rectify flaws in dental elements’ shape, alignment, or overly dark color. One of the most common dental aesthetic procedures is tooth whitening. This study attempts to introduce key aspects of bleaching and describe the different techniques and the mechanism of action of bleaching agents, focusing on the clinical implications in orthodontic and on composite restorations. Methods: The research was performed on PubMed, Web of Science, and Science Direct databases for articles on our topic published between 2017 and 2023, and we found a total of 1512 studies. In total, 57 papers were considered for the qualitative analysis in the review. Results: This study found that both carbamide peroxide and hydrogen peroxide were clinically effective, although patients’ level of tooth sensitivity seemed to be lessened by the latter. However, the latter appears to be more effective at reducing patient-experienced tooth sensitivity. Conclusion: Carbamide and hydrogen peroxide based whitening techniques were shown to be equally successful at treating tooth discoloration after bracket composite removal, with no discernible differences between them. To increase the effectiveness of whitening on composites, more research is needed. To achieve the desired results and avoid the negative effects of whitening gels on teeth and soft tissue, a patient-specific approach is advised.
Numerical Study of the Flow Characteristics of Downburst-like Wind over the 3D Hill Using Different Turbulence Models
Authors: Yan Bowen , Shen Ruifang , Ma Chenyan , Cheng Xu , Huang Guoqing , Yan Zhitao , Li Xiao , Zhang Zhigang
Published: June 2023

With the rapid development of computational fluid dynamics (CFD) technology, it has been widely used to study the wind field characteristics of downbursts in mountainous areas. However, there is little guidance on the selection of different turbulence models for simulating downburst wind fields over hills using CFD, and few comparative studies have been conducted. This paper used nine turbulence models to simulate the wind field of a downburst over a 3D quadratic ideal hill. The simulated values of average and transient winds were compared with wind tunnel test data, and the flow characteristics at different moments under a downburst were analyzed. The flow characteristics in the wake region of the downburst over the hill are also quantitatively analyzed using the proper orthogonal decomposition (POD) method. The results show that approximately 85% of the results from the LES and REA models fall within a 30% error range, so the large eddy simulation (LES) model and the realizable k-ε model (REA) are more accurate in simulating the mean wind field, and the transient wind field simulated by the LES model is also in good agreement with the experimental data. In addition, this paper reveals the evolution mechanism of the transient wind field structure over a hill model under a downburst and finds that the first-order mode obtained by POD may be related to the acceleration effect on the hilltop.
Enhanced Adaptable and Distributed Access Control Decision Making Model Based on Machine Learning for Policy Conflict Resolution in BYOD Environment
Authors: Ayedh M Aljuaid Turkea , Wahab Ainuddin Wahid Abdul , Idris Mohd Yamani Idna
Published: June 2023

Organisations are adopting new IT strategies such as “Bring Your Own Device” (BYOD) and remote working. These trends are highly beneficial both for enterprise owners and employees in terms of increased productivity and reduced costs. However, security issues such as unauthorised access as well as privacy concerns pose significant obstacles. These can be overcome by adopting access control techniques and a dynamic security and privacy policy that governs these issues where they arise. Policy decision points in traditional access control systems, such as role-based access control (RBAC), attribute-based access control (ABAC), or relationship-based access control (ReBAC), may be limited because the status of access control can vary in response to minor changes in user and resource properties. As a result, system administrators rely on a solution for constructing complex rules with many conditions and permissions for decision control. This results in access control issues, including policy conflicts, decision-making bottlenecks, delayed access response times and mediocre performance. This paper proposes a policy decision-making and access control-based supervised learning algorithm. The algorithm enhances policy decision points (PDPs). This is achieved by transforming the PDP’s problem into a binary classification for security access control that either grants or denies access requests. Also, a vector decision classifier based on the supervised machine learning algorithm is developed to generate an accurate, effective, distributed and dynamic policy decision point (PDP). Performance was evaluated using the Kaggle-Amazon access control policy dataset, which compared the effectiveness of the proposed mechanism to previous research benchmarks in terms of performance, time and flexibility. The proposed solution obtains a high level of privacy for access control policies because the PDP does not communicate directly with the policy administration point (PAP). In conclusion, PDP-based ML generates accurate decisions and can simultaneously fulfill multiple massive policies and huge access requests with 95% Accuracy in a short response time of around 0.15 s without policy conflicts. Access control security is improved by making it dynamic, adaptable, flexible and distributed.
Comprehensive Characterization of Oils and Fats of Six Species from the Colombian Amazon Region with Industrial Potential
Authors: Lozano-Garzón Kimberly , Orduz-Díaz Luisa L. , Guerrero-Perilla Camilo , Quintero-Mendoza Willian , Carrillo Marcela P. , Cardona-Jaramillo Juliana E. C.
Published: June 2023

The Colombian Amazon is a megadiverse region with high potential for commercial use in the pharmaceutical, food, and cosmetic industries, constantly expanding and looking for new alternatives from natural resources; unfortunately, few characterization reports of its profitable non-timber species in Colombia have been conducted. This work aimed to perform a comprehensive analysis of traditionally used species: Carapa guianensis (Andiroba), Euterpe precatoria (Asai), Mauritia flexuosa (Miriti), Astrocaryum murumuru (Murumuru), Plukenetia volubilis (Sacha Inchi), and Caryodendron orinocense H.Karst (Cacay). For this purpose, oil and fat quality indices, phytosterol, carotenoid, tocopherol, and tocotrienol content, as well as density and refractive index, were measured to establish their quality level. Multivariate analysis showed four groups of samples; such differences were mainly due to the composition rather than quality indices and physical properties, especially the content of saturated and unsaturated fatty acids. All species reported a precise composition, which makes them noninterchangeable, and Miriti oil arose as the most versatile ingredient for the industry. The Colombian Amazon region is a promising source of quality raw material, especially for oils/fats and unsaturated fatty acids; this resulted in the most interest for pharmaceutical, food, and cosmetic purposes.
Fungal Drug Discovery for Chronic Disease: History, New Discoveries and New Approaches
Authors: Prescott Thomas A. K. , Hill Rowena , Mas-Claret Eduard , Gaya Ester , Burns Edie
Published: June 2023

Fungal-derived drugs include some of the most important medicines ever discovered, and have proved pivotal in treating chronic diseases. Not only have they saved millions of lives, but they have in some cases changed perceptions of what is medically possible. However, now the low-hanging fruit have been discovered it has become much harder to make the kind of discoveries that have characterised past eras of fungal drug discovery. This may be about to change with new commercial players entering the market aiming to apply novel genomic tools to streamline the discovery process. This review examines the discovery history of approved fungal-derived drugs, and those currently in clinical trials for chronic diseases. For key molecules, we discuss their possible ecological functions in nature and how this relates to their use in human medicine. We show how the conservation of drug receptors between fungi and humans means that metabolites intended to inhibit competitor fungi often interact with human drug receptors, sometimes with unintended benefits. We also plot the distribution of drugs, antimicrobial compounds and psychoactive mushrooms onto a fungal tree and compare their distribution to those of all fungal metabolites. Finally, we examine the phenomenon of self-resistance and how this can be used to help predict metabolite mechanism of action and aid the drug discovery process.
Performance of 11 Host Biomarkers Alone or in Combination in the Diagnosis of Late-Onset Sepsis in Hospitalized Neonates: The Prospective EMERAUDE Study
Authors: Pons Sylvie , Trouillet-Assant Sophie , Subtil Fabien , Abbas-Chorfa Fatima , Cornaton Elise , Berthiot Amélie , Galletti Sonia , Plat Aurélie , Rapin Stephanie , Trapes Laurene , Generenaz Laurence , Brengel-Pesce Karen , Callies Arnaud , Plaisant Franck , Claris Olivier , Portefaix Aurelie , Flamant Cyril , Butin Marine
Published: June 2023

Despite the high prevalence of late-onset sepsis (LOS) in neonatal intensive care units, a reliable diagnosis remains difficult. This prospective, multicenter cohort study aimed to identify biomarkers early to rule out the diagnosis of LOS in 230 neonates ≥7 days of life with signs of suspected LOS. Blood levels of eleven protein biomarkers (PCT, IL-10, IL-6, NGAL, IP-10, PTX3, CD14, LBP, IL-27, gelsolin, and calprotectin) were measured. Patients received standard of care blinded to biomarker results, and an independent adjudication committee blinded to biomarker results assigned each patient to either infected, not infected, or unclassified groups. Performances of biomarkers were assessed considering a sensitivity of at least 0.898. The adjudication committee classified 22% of patients as infected and all of these received antibiotics. A total of 27% of the not infected group also received antibiotics. The best biomarkers alone were IL-6, IL-10, and NGAL with an area under the curve (95% confidence interval) of 0.864 (0.798–0.929), 0.845 (0.777–0.914), and 0.829 (0.760–0.898), respectively. The best combinations of up to four biomarkers were PCT/IL-10, PTX3/NGAL, and PTX3/NGAL/gelsolin. The best models of biomarkers could have identified not infected patients early on and avoided up to 64% of unjustified antibiotics. At the onset of clinical suspicion of LOS, additional biomarkers could help the clinician in identifying non-infected patients.
Determination of the Minimum Sample Amount for Capillary Electrophoresis-Fourier Transform Mass Spectrometry (CE-FTMS)-Based Metabolomics of Colorectal Cancer Biopsies
Authors: Sugishita Tetsuo , Tokunaga Masanori , Kami Kenjiro , Terai Kozue , Yamamoto Hiroyuki , Shinohara Hajime , Kinugasa Yusuke
Published: June 2023

The minimum sample volume for capillary electrophoresis-Fourier transform mass spectrometry (CE-FTMS) useful for analyzing hydrophilic metabolites was investigated using samples obtained from colorectal cancer patients. One, two, five, and ten biopsies were collected from tumor and nontumor parts of the surgically removed specimens from each of the five patients who had undergone colorectal cancer surgery. Metabolomics was performed on the collected samples using CE-FTMS. To determine the minimum number of specimens based on data volume and biological interpretability, we compared the number of annotated metabolites in each sample with different numbers of biopsies and conducted principal component analysis (PCA), hierarchical cluster analysis (HCA), quantitative enrichment analysis (QEA), and random forest analysis (RFA). The number of metabolites detected in one biopsy was significantly lower than those in 2, 5, and 10 biopsies, whereas those detected among 2, 5, and 10 pieces were not significantly different. Moreover, a binary classification model developed by RFA based on 2-biopsy data perfectly distinguished tumor and nontumor samples with 5- and 10-biopsy data. Taken together, two biopsies would be sufficient for CE-FTMS-based metabolomics from a data content and biological interpretability viewpoint, which opens the gate of biopsy metabolomics for practical clinical applications.
TLR7 Agonist GS–9620 Combined with Nicotinamide Generate Viral Reactivation in Seronegative SHIVSF162P3-Infected Rhesus Monkeys
Authors: Cong Zhe , Sun Yuting , Dang Cui , Yang Chenbo , Zhang Jingjing , Lu Jiahan , Chen Ting , Wei Qiang , Wang Wei , Xue Jing
Published: June 2023

Antiretroviral therapy is capable of inhibiting HIV replication, but it fails to completely achieve a cure due to HIV persistence. The commonly used HIV cure approach is the “shock and kill” strategy, which employs latency-reversing agents to trigger viral reactivation and boost cellular immunity. Finding the appropriate drug combination for the “shock and kill” strategy would greatly facilitate clinical trials. The toll-like receptor (TLR) 7 agonist GS–9620 and nicotinamide (NAM) are reported as potential latency-reversing agents. Herein, we found the absence of viral reactivation when SHIVSF162P3-aviremic rhesus macaques were treated with GS–9620 monotherapy. However, our findings demonstrate that viral blips emerged in half of the macaques treated with the combination therapy of GS–9620 and NAM. Notably, an increase in the reactivation of the replication-competent latent virus was measured in monkeys treated with the combination therapy. These findings suggest that the GS–9620 and NAM combination could be used as a multipronged HIV latency stimulation approach, with potential for optimizing antiviral therapy design.
Nickel Oxy-Hydroxy/Multi-Wall Carbon Nanotubes Film Coupled with a 3D-Printed Device as a Nonenzymatic Glucose Sensor
Authors: Silva Murillo N. T. , Rocha Raquel G. , Richter Eduardo M. , Munoz Rodrigo A. A. , Nossol Edson
Published: June 2023

A rapid and simple method for the amperometric determination of glucose using a nanocomposite film of nickel oxyhydroxide and multi-walled carbon nanotube (MWCNTs) was evaluated. The NiHCF)/MWCNT electrode film was fabricated using the liquid–liquid interface method, and it was used as a precursor for the electrochemical synthesis of nickel oxy-hydroxy (Ni(OH)2/NiOOH/MWCNT). The interaction between nickel oxy-hydroxy and the MWCNTs provided a film that is stable over the electrode surface, with high surface area and excellent conductivity. The nanocomposite presented an excellent electrocatalytic activity for the oxidation of glucose in an alkaline medium. The sensitivity of the sensor was found to be 0.0561 μA μmol L−1, and a linear range from 0.1 to 150 μmol L−1 was obtained, with a good limit of detection (0.030 μmol L−1). The electrode exhibits a fast response (150 injections h−1) and a sensitive catalytic performance, which may be due to the high conductivity of MWCNT and the increased active surface area of the electrode. Additionally, a minimal difference in the slopes for ascending (0.0561 µA µmol L−1) and descending (0.0531 µA µmol L−1) was observed. Moreover, the sensor was applied to the detection of glucose in artificial plasma blood samples, achieving values of 89 to 98% of recovery.
Do Individuals with Spinal Cord Injury Benefit from Semi-Immersive Virtual Reality Cognitive Training? Preliminary Results from an Exploratory Study on an Underestimated Problem
Authors: Maggio Maria Grazia , Bonanno Mirjam , Manuli Alfredo , Onesta Maria Pia , De Luca Rosaria , Quartarone Angelo , Calabrò Rocco Salvatore
Published: June 2023

A spinal cord injury (SCI) is damage to any part of the spinal cord, caused by traumatic or non-traumatic events. Clinically, SCI is associated with partial or complete loss of motor, sensory, and autonomic functions below the site of injury. However, cognitive alterations in specific domains can also occur. The aim of this study was to evaluate the effects of semi-immersive virtual reality (VR) cognitive training (using the BTS Nirvana, Italy) in promoting global functional recovery in patients with SCI. Forty-two SCI patients were included in this retrospective case-control study, and the analysis was carried out using an electronic data retrieval system. The enrolled patients were divided into two groups with the same demographic and medical characteristics: the control group (CG: 21 patients) participated in traditional therapy, whereas the experimental group (EG: 21 patients) received training using semi-immersive VR. In both groups, there were patients with A- or B-grade impairments according to the American Spinal Injury Association (ASIA) scale. Both study groups underwent the same amount of cognitive training (but using a different type of training: conventional vs. innovative), consisting of three weekly sessions for eight weeks (24 sessions in total), each session lasting approximately sixty minutes, as well as the same amount of physiotherapy. The effect of the two treatments (EG/CG) was significantly different in global cognitive functioning (MOCA: p = 0.001), mood (BDI: p = 0.006), and overall quality of life (SF12 Total: p < 0.001), especially in physical perception (SF12-Physics: p = 0.004). Our results suggest that SCI patients could benefit from cognitive training using semi-immersive VR. Indeed, the integration of cognitive exercises that require movement and provide increased feedback could allow for better motor and cognitive recovery in people with SCI.
Food and Development: Children and Adolescents with Neurodevelopmental and Comorbid Eating Disorders—A Case Series
Authors: Pruccoli Jacopo , Guardi Giulia , La Tempa Angela , Valeriani Beatrice , Chiavarino Francesca , Parmeggiani Antonia
Published: June 2023

The impact of psychiatric comorbidities in the diagnosis and treatment of feeding and eating disorders (FEDs) represents an emerging research topic. The current literature, nonetheless, lacks studies investigating the developmental paths of individuals with FEDs and comorbid neurodevelopmental disorders (NDDs). Here, we report 11 cases of children and adolescents with comorbid FEDs and NDDs, as assessed along the neuropsychological, psychopathological, and nutritional developmental pathways. The onset of FED-related psychopathology was preceded, sometimes undiagnosed, by altered neurodevelopmental features leading to specific NDD diagnoses (autism spectrum disorder—ASD; attention-deficit/hyperactivity disorder—ADHD; specific learning disorder—SLD). NDDs appeared to influence the diagnoses and treatments of FEDs, frequently with an impact on socio-relational and emotional premorbid features, and on the possibility to receive and attend FED-targeted treatments. Further studies should longitudinally contribute to assessing the experiences of care and neurodevelopmental pathways of children with FEDs and specific NDD comorbidities.
Shading Calculation Methods and Regulation Simplifications—The Portuguese Case
Authors: Oliveira Marta Fernandes , Mendonça Paulo , Tenpierik Martin , Santiago Pedro , Silva José F. , Silva Lígia Torres
Published: June 2023

How to analyse the omissions of thermal regulations and evaluate methodologies that provide building execution or thermal certificates that do not correspond to reality and usually incur costs? We can start by analysing different simulation methods and shading calculations that provide solar gains and shadow optimisation. After evaluating how the regulations define the calculation assumptions and how this calculation is performed, the discrepancies (simplifications) that the regulations allow or ignore are presented, and it is exemplified using two case studies. Using the Portuguese regulation as a case study, it leads to incorrect conclusions or assumptions due to unequal access to solar radiation or the shading factor calculation that experiences the omission of angles or time periods. Therefore, the aim is to propose a calculation process (premises) that minimises the discrepancies between simulation (optimisation strategy) and reality (applicability of strategies) for sustainable output.
Residential Energy Consumption-A Computational Bibliometric Analysis
Authors: Quintana Daniela I. , Cansino José M.
Published: June 2023

This paper conducted a systematic literature review (SLR) of peer-review documents focused on residential energy consumption. The main finding of this SLR derived from its computational implementation, filling a gap in the available literature. The paper had two main objectives: (i) the SLR itself; and (ii) to systematically identify the main policy measures oriented at reducing energy consumption recommended by the available literature and its related greenhouses gas emissions. A significant lack of a connection between scientific fields was identified. The fields of energy, engineering and environmental science are those that have addressed these topics the most. The sub-field with the largest presence is energy, with 29.5%, followed by engineering (23.9%) and the environment (21.3%). It is recommended that this gap be bridged because many of the main political measures in force to reduce residential energy consumption come from economics and need to be explored from a social science perspective to choose those measures that are more cost-effective.
The Role of Ion Exchange Resins for Solving Biorefinery Catalytic Processes Challenges
Authors: Patiño Yolanda , Faba Laura , Peláez Raquel , Cueto Jennifer , Marín Pablo , Díaz Eva , Ordóñez Salvador
Published: June 2023

Different possible applications of ion exchange resins in the framework of biorefinery catalytic applications are discussed in this article. Three case studies were selected for this approach, connected to three main routes for biomass upgrading: syngas upgrading to high-value chemicals, biomass hydrolysate upgrading, and direct upgrading of oily fraction. The tailored acidic properties of these materials, as well as their stability in the presence of water, have made them promising catalysts for applications such as obtaining biodiesel from activated sludge wastes in batch reactors and obtaining polyoxymethylene methyl ether from syngas (via formaldehyde and methylal, and working in a continuous fixed bed reactor). However, the acidity of these materials may still be too low for acid-catalyzed aldol condensation reactions in the aqueous phase.
Effects of Mott–Schottky Frequency Selection and Other Controlling Factors on Flat-Band Potential and Band-Edge Position Determination of TiO2
Authors: Lee Siaw Foon , Jimenez-Relinque Eva , Martinez Isabel , Castellote Marta
Published: June 2023

The use of titanium dioxide for tackling environmental pollution has attracted great research interest recently. The potential of a photocatalyst in removing contaminants depends mainly on its conduction and valence-band edges relative to the standard potential of reactive oxygen species. One of the methods used in determining these band-edge positions is via the Mott–Schottky analysis. Thus, the aim of this work was to investigate the influence of the Mott–Schottky frequency and different electrochemical conditions on flat-band potential values and band-edge positions of pure anatase or Degussa P25, calcined or uncalcined in a 0.2 M Na2SO4 solution. The results showed that the shift in the flat-band potential was not so frequency-dependent in the Mott–Schottky analysis, however, other reasons, such as immersion duration leading to thenardite Na2SO4 salt deposition on the surface, irradiation of sources and the change in the pH of the solution (in the range of 1.64–12.11) were also responsible for it. In general, both the calcined anatase and P25 had a less negative value of the flat-band potential compared to the uncalcined. On the other hand, the calcined anatase had a tendency to have a less negative value of the flat-band potential than the calcined P25. From this study, the frequency range for obtaining the flat-band potential within one standard deviation in the Mott–Schottky at a single-frequency analysis was found to be between 200 and 2000 Hz. The energy difference between the Fermi level and the conduction band edge for anatase and P25, either calcined or uncalcined, was 0.097–0.186 and 0.084–0.192 eV, respectively. On the other hand, the band-edge position of anatase or P25 tended to shift upwards when it was repeatedly used in the photoelectrochemical analysis.
Nitrate, Auxin and Cytokinin—A Trio to Tango
Authors: Abualia Rashed , Riegler Stefan , Benkova Eva
Published: June 2023

Nitrogen is an important macronutrient required for plant growth and development, thus directly impacting agricultural productivity. In recent years, numerous studies have shown that nitrogen-driven growth depends on pathways that control nitrate/nitrogen homeostasis and hormonal networks that act both locally and systemically to coordinate growth and development of plant organs. In this review, we will focus on recent advances in understanding the role of the plant hormones auxin and cytokinin and their crosstalk in nitrate-regulated growth and discuss the significance of novel findings and possible missing links.
Familial Adult Myoclonus Epilepsy: A Non-Coding Repeat Expansion Disorder of Cerebellar–Thalamic–Cortical Loop
Authors: Cuccurullo Claudia , Striano Pasquale , Coppola Antonietta
Published: June 2023

Familial adult myoclonus Epilepsy (FAME) is a non-coding repeat expansion disorder that has been reported under different acronyms and initially linked to four main loci: FAME1 (8q23.3–q24.1), FAME 2 (2p11.1–q12.1), FAME3 (5p15.31–p15.1), and FAME4 (3q26.32–3q28). To date, it is known that the genetic mechanism underlying FAME consists of the expansion of similar non-coding pentanucleotide repeats, TTTCA and TTTTA, in different genes. FAME is characterized by cortical tremor and myoclonus usually manifesting within the second decade of life, and infrequent seizures by the third or fourth decade. Cortical tremor is the core feature of FAME and is considered part of a spectrum of cortical myoclonus. Neurophysiological investigations as jerk-locked back averaging (JLBA) and corticomuscular coherence analysis, giant somatosensory evoked potentials (SEPs), and the presence of long-latency reflex I (or C reflex) at rest support cortical tremor as the result of the sensorimotor cortex hyperexcitability. Furthermore, the application of transcranial magnetic stimulation (TMS) protocols in FAME patients has recently shown that inhibitory circuits are also altered within the primary somatosensory cortex and the concomitant involvement of subcortical networks. Moreover, neuroimaging studies and postmortem autoptic studies indicate cerebellar alterations and abnormal functional connectivity between the cerebellum and cerebrum in FAME. Accordingly, the pathophysiological mechanism underlying FAME has been hypothesized to reside in decreased sensorimotor cortical inhibition through dysfunction of the cerebellar–thalamic–cortical loop, secondary to primary cerebellar pathology. In this context, the non-coding pentameric expansions have been proposed to cause cerebellar damage through an RNA-mediated toxicity mechanism. The elucidation of the underlying pathological mechanisms of FAME paves the way to novel therapeutic possibilities, such as RNA-targeting treatments, possibly applicable to other neurodegenerative non-coding disorders.
The Mediating Role of Callous–Unemotional Traits in the Relationship between Parental Aggression and Behavioral Problems among Chinese Preschoolers
Authors: Zhang Gengli , Zhu Yantong
Published: June 2023

It is well known that aggressive parenting is associated with behavioral problems among Western children in their early childhood, but this has rarely been examined among Chinese preschoolers. The purpose of this study is to explore the relationship between aggressive parenting, callous–unemotional traits (CU traits), and behavioral problems among a large Chinese preschool sample. Data were collected in Wuhu city, China, from 1879 preschoolers (54% of whom were male) with a mean age of 65.66 months (standard deviation = 9.41). Parents provided information about the frequency of aggressive parenting, children’s behaviors, and demographic characteristics via an online questionnaire. Mediation models were applied to analyze the associations between aggressive parenting, CU traits, and behavioral problems. Preschoolers’ age, gender, and family socioeconomic status were considered as covariates. The findings of our study revealed that higher frequencies of parental psychological and physical aggression were associated with high levels of CU traits, which were related to increased levels of preschoolers’ behavioral problems. This study extends previous studies by revealing a positive relationship between aggressive parenting and behavioral problems among Chinese preschoolers via CU traits and highlights the risks of aggressive parenting. Interventions for improving parenting strategies and lessening callous–unemotional traits should be developed to help reduce behavioral problems.