bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of High Erucic Acid Brassica carinata Genotypes through Multi-Trait Stability Index
Authors: Tesfaye Misteru , Feyissa Tileye , Hailesilassie Teklehaimanot , Mengistu Birhanu , Kanagarajan Selvaraju , Zhu Li-Hua
Published: July 2024

Brassica carinata is an important and native oilseed crop in Ethiopia. The seed oil from B.carinata attracts global attention for its various industrial applications, mainly due to its high erucic acid levels and its superior agronomic traits. Since the demand for high erucic acid from oilseed brassica has been increasing in the world market due to its wider applications in bio-industries, the breeding target of B. carinata has recently been focused on enhancing its erucic acid. Several high erucic acid B. carinata genotypes have been screened from the pre-breeding activities. Such genotypes, however, need to be tested for their stable performance, for their erucic acid level, and other desirable traits under different environments. The aim of this study was to identify high erucic acid B. carinata genotypes with stable performance in multiple desirable traits. Thirty-two B. carinata genotypes were grown in a randomized complete block design with three replications at three locations for two years. The genotypes were evaluated for nine desirable traits related to seed oil quality (erucic acid and oil content), seed yield, and other agronomic traits. The results showed that the proportion of genotype by environment interaction (GEI) was clearly observed in erucic acid, which led to a stability and mean performance analysis for selecting the most stable and best-performing genotypes for the desired traits. For such an analysis, we used the multi-trait stability index (MTSI) along with the weighted average of absolute score BLUPs (WAASB). As revealed from the MTSI, five genotypes (G13, G18, G10, G22 and G5) were identified as the most stable in erucic acid, oil content, seed yield, and other agronomic traits. The selected genotypes showed on average 45.7% erucic acid, 3185 kg ha−1 seed yield and 45.1% oil content with 4.3%, 25.8% and 6.9% positive selection gain, respectively. The negative selection gain of phenological traits and the plant height of the selected genotypes revealed their early maturity and their lower probability of being affected by lodging. Our findings demonstrated MTSI can be used to select high erucic acid B. carinata with a set of desirable traits, which would facilitate breeding efforts in developing novel and high erucic acid B. carinata varieties. Our results also showed that MTSI is an effective tool for selecting genotypes across different environments due to its unique ability to select multiple traits simultaneously.
Assessment of Climate Adaptability in the Late-Maturing Citrus Industry in Sichuan Province
Authors: He Yu
Published: July 2024

Sichuan Province is the largest inland area for late-maturing citrus fruit production in China, and its climate conditions are a primary consideration for the cultivation of late-maturing citrus fruits. Based on meteorological data from 2010 to 2020 for the 18 prefecture-level cities and autonomous prefectures in Sichuan Province that cultivate late-maturing citrus fruits, along with the traditional method of dividing the advantages of citrus and the calculation of comparative advantage using factor endowment coefficients, we identified the annual average temperature, annual accumulated temperature ≥ 10 °C, average temperatures in July and January, annual precipitation, and annual sunshine hours as input indicators. We selected the resource endowment coefficient as the output indicator and used the DEA–Malmquist index model to evaluate the climate adaptability of Sichuan’s late-maturing citrus fruit industry. The analysis results indicate that the overall climate conditions in Sichuan are suitable for the growth of late-maturing citrus fruits. However, extensive cultivation of similar varieties has led to a decline in resource endowment across different regions. The use of arable land for cultivating late-maturing citrus fruits has also reduced climate adaptability. Policies that contradict climate adaptability do not support sustainable development within Sichuan’s late-maturing citrus fruit industry.
Diversified Cover Crops and No-Till Enhanced Soil Total Nitrogen and Arbuscular Mycorrhizal Fungi Diversity: A Case Study from the Karst Area of Southwest China
Authors: Tian Lihua , Wang Tao , Cui Song , Li Yuan , Gui Weiyang , Yang Feng , Chen Jihui , Dong Rui , Gu Xinyao , Zhao Xuechun , Zhang Mingjun , Chen Chao , Li Zhou
Published: July 2024

The deteriorating soil health under continuous monoculture is commonly found across various cropping systems. This study evaluated the effects of different tillage practices (conventional tillage and no till) and species mixtures (legumes and grasses) on arbuscular mycorrhizal fungi (AMF) community properties, soil nutrients, and enzyme activity in a 3-year experiment. Compared with traditional tillage, the number of AMF species under no-till conditions was increased, with the Glomus group being dominant. Under different tillage conditions, TN (total N) and AN (available N) contents under no till were significantly higher than those under conventional tillage, while no significant differences among other nutrients were found. The activities of soil acid phosphatase (S-ACP), soil dehydrogenase (S-DHA), and soil sucrose (S-SC) under conventional tillage were significantly higher than those under no till, and the cover crop mixtures also had an exclusive advantage in yield. Soil organic matter (SOM) indicated a significant negative correlation with glomalin-related soil protein (GRSP). The increase in diversity associated with the AMF species community was strongly correlated with the increase in three enzyme activities, and AN was negatively correlated with all species. Tillage did not significantly change soil chemistry, except for AN, and the high concentration of AN led to a decrease in AMF species. The results of this study showed that no till was an effective measure for enriching soil micro-organism population. Additionally, soil AMF diversity was improved by cover crop mixtures, and microbial diversity was higher than that under monoculture cover crops. Different AMF groups responded differently to tillage and cover crop mixtures. Across all mixtures, the combination of hairy vetch (Vicia villosa R.) and ryegrass (Lolium perenne L.) performed the best.
Absorption and Xylem Transport of 33P-Labeled Phosphorus in Nodulated Soybean Plants
Authors: Yamamura Yoshiaki , Nara Seiya , Higuchi Kyoko , Saito Akihiro , Ohyama Takuji
Published: July 2024

Phosphorus (P) is an essential macro-element for plants, and understanding the characteristics of the absorption and transport of P in crops is essential. The low availability of P restricts the growth, nitrogen fixation, and yield of soybean plants. In this research, the radioisotope 33P was supplied to the culture solution to trace the absorption and transport of P in nodulated soybean plants monitored using an imaging plate. The absorption rate of 33P was almost the same under the light and dark conditions. The absorption rate of 33P in the decapitated roots was near to that of the intact plants under light. These results indicate that the P absorption is not affected by evapotranspiration over a short time period. Conversely, the 33P transport from the roots to the shoot was significantly lower under dark conditions than it was under light conditions, although some 33P reached the top of the shoots under both the light and dark conditions. The transport of P to the shoots depends on the transpiration supplemented by the root pressure. The multiplication value of the 33P concentration in the xylem sap and transpiration rate was almost equivalent to the transport rate of 33P in the intact shoots. This value may be adaptable and used to estimate the transport rate of P for the diagnosis.
Perceived Status and Sustainable Actions: How Subjective Socioeconomic Status Drives Green Energy Consumption in Chinese Households
Authors: Ren Yi , Zhuang Linmei , Xu Dingde
Published: July 2024

Household energy consumption plays a critical role in the context of global climate change. Utilizing data from the 2018 China Social Survey (CGSS), this study empirically examined the impact of subjective socioeconomic status (SES) on household green energy consumption behaviors using probit and ordered probit models. The mechanism of influence was further analyzed through the mediated effect approach. The results found include the following: (1) Although the proportion of households participating in green energy policies is similar to those not participating, the proportion of households deeply participating in multiple policies is very low; (2) subjective SES significantly influences both the rate and depth of household participation in green energy policies; (3) internet usage and understanding of green energy policies serve as mediating mechanisms for the promotive effect of subjective SES; and (4) subjective SES showed significant heterogeneity in its effects on different gender and education level groups. These findings contribute to the understanding of the drivers of household green energy use decisions and provide an important reference for governmental policymaking to enhance participation rates and degrees in green energy participation. Implications of these findings highlight the potential for targeted policies that address internet accessibility and educational outreach, which could significantly enhance the effectiveness of green energy initiatives across diverse socioeconomic groups.
Briquette Production from Vineyard Winter Pruning Using Two Different Approaches
Authors: Ţenu Ioan , Roșca Radu , Corduneanu Oana-Raluca , Roman Cecilia , Senila Lacrimioara , Arsenoaia Vlad , Butnaru Liviu , Băetu Marius , Chirilă Constantin , Cârlescu Petru Marian
Published: July 2024

Worldwide, different strategies are being developed in order to ensure optimum conditions for the development and growth of economic competitiveness, as well as for increasing the quality of life and environmental protection. All these strategies are closely linked to the development and modernization of systems for producing energy from clean and renewable sources. In this context, the present paper presents the results of research regarding the evaluation of the sustainability of briquette production using biomass resulting from vine winter pruning as the raw material. An analysis of the scientific literature indicates that nearly 8 Mt of biomass would result from the over 7.4 million hectares of vine plantations in the world, biomass that could be valorized through densification in order to produce solid biofuels with a lower calorific value of more than 17 MJ/kg. This study examines the production of briquettes from vineyard winter pruning with consideration of two types of densification technologies: baling and natural drying of the tendrils, and collection, shredding, and artificial drying of the lignocellulose debris. The quality indices and energy consumption and energy efficiency of the briquettes were evaluated to determine their feasibility as an alternative fuel source. When designing the scientific endeavor, the following aspects were considered: defining the aim and objectives of the research; designing the research algorithm; collecting, preparing, and conditioning the biomass; conducting a chemical analysis of the briquettes; and evaluating the energy consumption and energy efficiency for producing the briquettes, taking into account two drying methods (natural and artificial drying). In the meantime, some specific laboratory equipment was designed and built for the artificial drying of biomass, evaluation of mechanical durability, measurement of energy consumption, etc. Analysis of the experimental data has led to the conclusion that the agricultural waste from vine pruning can constitute an important and sustainable source of energy in the form of briquettes that fulfill most of the requirements imposed by international standards.
Residual Mulching Film Detection in Seed Cotton Using Line Laser Imaging
Authors: Wang Sanhui , Zhang Mengyun , Wen Zhiyu , Zhao Zhenxuan , Zhang Ruoyu
Published: July 2024

Due to the widespread use of mulching film in cotton planting in China, residual mulching film mixed with machine-picked cotton poses a significant hazard to cotton processing. Detecting residual mulching film in seed cotton has become particularly challenging due to the film’s semi-transparent nature. This study constructed an imaging system combining an area array camera and a line scan camera. A detection scheme was proposed that utilized features from both image types. To simulate online detection, samples were placed on a conveyor belt moving at 0.2 m/s, with line lasers at a wavelength of 650 nm as light sources. For area array images, feature extraction was performed to establish a partial least squares discriminant analysis (PLS-DA) model. For line scan images, texture feature analysis was used to build a support vector machine (SVM) classification model. Subsequently, image features from both cameras were merged to construct an SVM model. Experimental results indicated that detection methods based on area array and line scan images had accuracies of 75% and 79%, respectively, while the feature fusion method achieved an accuracy of 83%. This study demonstrated that the proposed method could effectively improve the accuracy of residual mulching film detection in seed cotton, providing a basis for reducing residual mulching film content during processing.
Research Progress on Diseases Caused by the Soil-Borne Fungal Pathogen Rhizoctonia solani in Alfalfa
Authors: Akber Muhammad Abdullah , Fang Xiangling
Published: July 2024

Rhizoctonia solani is a soil-borne fungal species with worldwide distribution and poses serious threats to a wide range of economically important crops such as grain and forage crops. This pathogen has survival capabilities within plants and soil, giving rise to sclerotia and persisting for several months or years. Alfalfa (Medicago sativa) is the most widely grown and important forage crop in China and worldwide. The unique characteristics of alfalfa, such as excellent forage quality, ruminant desirability, and substantial biomass output, distinguish it from other fodder crops, and it is also known as the “Queen of Forages”. However, the production of alfalfa is seriously affected by R. solani, with yield losses of 20% to 60% globally. This review firstly summarizes diseases such as seedling damping-off, root rot, crown rot, root cankers, stem cankers, blight, and stem rot caused by R. solani in alfalfa and the survival mechanism of this pathogen. The techniques for R. solani detection and quantification from plants and soils, as well as management through host resistance, cultural practices, fungicides, and biological control, were then overviewed. This review provides scientific knowledge to enable researchers to efficiently manage R. solani in alfalfa production.
Predicting Number of Vehicles Involved in Rural Crashes Using Learning Vector Quantization Algorithm
Authors: Shaffiee Haghshenas Sina , Guido Giuseppe , Shaffiee Haghshenas Sami , Astarita Vittorio
Published: July 2024

Roads represent very important infrastructure and play a significant role in economic, cultural, and social growth. Therefore, there is a critical need for many researchers to model crash injury severity in order to study how safe roads are. When measuring the cost of crashes, the severity of the crash is a critical criterion, and it is classified into various categories. The number of vehicles involved in the crash (NVIC) is a crucial factor in all of these categories. For this purpose, this research examines road safety and provides a prediction model for the number of vehicles involved in a crash. Specifically, learning vector quantization (LVQ 2.1), one of the sub-branches of artificial neural networks (ANNs), is used to build a classification model. The novelty of this study demonstrates LVQ 2.1’s efficacy in categorizing accident data and its ability to improve road safety strategies. The LVQ 2.1 algorithm is particularly suitable for classification tasks and works by adjusting prototype vectors to improve the classification performance. The research emphasizes how urgently better prediction algorithms are needed to handle issues related to road safety. In this study, a dataset of 564 crash records from rural roads in Calabria between 2017 and 2048, a region in southern Italy, was utilized. The study analyzed several key parameters, including daylight, the crash type, day of the week, location, speed limit, average speed, and annual average daily traffic, as input variables to predict the number of vehicles involved in rural crashes. The findings revealed that the “crash type” parameter had the most significant impact, whereas “location” had the least significant impact on the occurrence of rural crashes in the investigated areas.
Modeling Sea Level Rise Using Ensemble Techniques: Impacts on Coastal Adaptation, Freshwater Ecosystems, Agriculture and Infrastructure
Authors: Dhal Sambandh Bhusan , Singh Rishabh , Pandey Tushar , Dey Sheelabhadra , Kalafatis Stavros , Kesireddy Vivekvardhan
Published: July 2024

Sea level rise (SLR) is a crucial indicator of climate change, primarily driven by greenhouse gas emissions and the subsequent increase in global temperatures. The impact of SLR, however, varies regionally due to factors such as ocean bathymetry, resulting in distinct shifts across different areas compared to the global average. Understanding the complex factors influencing SLR across diverse spatial scales, along with the associated uncertainties, is essential. This study focuses on the East Coast of the United States and Gulf of Mexico, utilizing historical SLR data from 1993 to 2023. To forecast SLR trends from 2024 to 2103, a weighted ensemble model comprising SARIMAX, LSTM, and exponential smoothing models was employed. Additionally, using historical greenhouse gas data, an ensemble of LSTM models was used to predict real-time SLR values, achieving a testing loss of 0.005. Furthermore, conductance and dissolved oxygen (DO) values were assessed for the entire forecasting period, leveraging forecasted SLR trends to evaluate the impacts on marine life, agriculture, and infrastructure.
Challenges of Using Synthetic Data Generation Methods for Tabular Microdata
Authors: Miletic Marko , Sariyar Murat
Published: July 2024

The generation of synthetic data holds significant promise for augmenting limited datasets while avoiding privacy issues, facilitating research, and enhancing machine learning models’ robustness. Generative Adversarial Networks (GANs) stand out as promising tools, employing two neural networks—generator and discriminator—to produce synthetic data that mirrors real data distributions. This study evaluates GAN variants (CTGAN, CopulaGAN), a variational autoencoder, and copulas on diverse real datasets of different complexity encompassing numerical and categorical attributes. The results highlight CTGAN’s sensitivity to training parameters and TVAE’s robustness across datasets. Scalability challenges persist, with GANs demanding substantial computational resources. TVAE stands out for its high utility across all datasets, even for high-dimensional data, though it incurs higher privacy risks, which is indicative of the curse of dimensionality. While no single model universally excels, understanding the trade-offs and leveraging model strengths can significantly enhance synthetic data generation (SDG). Future research should focus on adaptive learning mechanisms, scalability enhancements, and standardized evaluation metrics to advance SDG methods effectively. Addressing these challenges will foster broader adoption and application of synthetic data.
Enhancement of Yaw Moment Control for Drivers with Excessive Steering in Emergency Lane Changes
Authors: Peng Shou-Tao , Chen Chih-Keng , Sheu Yih-Ran , Chang Yu-Chun
Published: July 2024

When a ground vehicle runs at high speeds, even a slight excess in the wheel steering angle can immediately cause the vehicle to slide sideways and lose control. In this study, we propose an active safety control system designed to address emergency situations where the driver applies excessive steering input and the vehicle speed varies significantly during control. The system combines the direct yaw moment (DYM) method with a steering saturation scheme that prevents excessive driver steering input from adversely influencing the front-wheel steering. Consequently, the control system allows the DYM to focus more on other stabilization tasks and maintain tire/road friction within its workable linear range. The implementation relies on a reference steering angle and a reference vehicle state, derived from a linear vehicle model considering tire/road friction limitations. When the driver’s steering angle and the system state deviate from these reference values, the control system intervenes by applying both the steering saturation scheme and DYM method. This ensures the front-wheel steering angle and system state remain close to the reference values. The control strategy is developed using the polytopic Linear Parameter Varying (LPV) technique and Linear Matrix Inequality (LMI) to account for the changes in vehicle speed. It is further enhanced with an input saturation technique based on a high-gain approach, which improves control utilization and system response during emergency situations. The advantages of the proposed control strategy are demonstrated through simulation results.
A Comprehensive Review on the Antioxidant and Anti-Inflammatory Bioactives of Kiwi and Its By-Products for Functional Foods and Cosmetics with Health-Promoting Properties
Authors: Moysidou Anastasia Maria , Cheimpeloglou Konstantina , Koutra Spyridoula Ioanna , Finos Marios Argyrios , Ofrydopoulou Anna , Tsoupras Alexandros
Published: July 2024

Kiwi’s increased popularity as a healthy fruit with several agro-food applications has increased the amount of bio-waste produced like leaf, peel, and seed by-products, usually combined to form a kiwi pomace, which increases the environmental footprint of kiwi fruit and waste management costs. The aim of the present study is to thoroughly review and outline the nutritional content and bioactive components of both kiwi fruit and its by-products, as well as the innovative approaches to obtain and valorize kiwi’s bioactives, phytochemicals, vitamins, and nutrients in several functional food products, nutraceuticals, and cosmetics applications with health-promoting properties. The antioxidant and anti-inflammatory properties and mechanisms of action of the extracted polyphenols, flavonoids, flavones, organic acids, and other bioactive components in both the fruit and in its functional products are also elucidated. Emphasis is given to those bioactive ingredients and extracts from kiwi by-products that can be valorized in various functional foods, supplements, nutraceuticals, nutricosmetics, cosmeceuticals, and cosmetics-related applications, with antioxidant and anti-inflammatory health-promoting properties. Characteristic examples with reported health benefits are the functional kiwi fruit jelly (FKJ),fermented kiwi fruit products like wine, starchy kiwi fruit flour (SKF), and kiwi-derived functional protein bars, cheese and flour, as well as several nutraceuticals and functional cosmetics with kiwi bioactives improving their antioxidant, antiaging, and photoprotective properties, collagen synthesis, skin density, hydration, elasticity, and the wound healing process, while beneficially reducing skin roughness, wrinkles, hyperpigmentation, keratinocyte death, and DNA and cell damage. The limitations and future perspectives for these kiwi bioactive-based applications are also discussed.
The State of the Art of Artificial Intelligence Applications in Eosinophilic Esophagitis: A Systematic Review
Authors: Votto Martina , Rossi Carlo Maria , Caimmi Silvia Maria Elena , De Filippo Maria , Di Sabatino Antonio , Lenti Marco Vincenzo , Raffaele Alessandro , Marseglia Gian Luigi , Licari Amelia
Published: July 2024

Introduction: Artificial intelligence (AI) tools are increasingly being integrated into computer-aided diagnosis systems that can be applied to improve the recognition and clinical and molecular characterization of allergic diseases, including eosinophilic esophagitis (EoE). This review aims to systematically evaluate current applications of AI, machine learning (ML), and deep learning (DL) methods in EoE characterization and management. Methods: We conducted a systematic review using a registered protocol published in the International Prospective Register of Systematic Reviews (CRD42023451048). The risk of bias and applicability of eligible studies were assessed according to the prediction model study risk of bias assessment tool (PROBAST). We searched PubMed, Embase, and Web of Science to retrieve the articles. The literature review was performed in May 2023. We included original research articles (retrospective or prospective studies) published in English in peer-reviewed journals, studies whose participants were patients with EoE, and studies assessing the application of AI, ML, or DL models. Results: A total of 120 articles were found. After removing 68 duplicates, 52 articles were reviewed based on the title and abstract, and 34 were excluded. Eleven full texts were assessed for eligibility, met the inclusion criteria, and were analyzed for the systematic review. The AI models developed in three studies for identifying EoE based on endoscopic images showed high score performance with an accuracy that ranged from 0.92 to 0.97. Five studies developed AI models that histologically identified EoE with high accuracy (87% to 99%). We also found two studies where the AI model identified subgroups of patients according to their clinical and molecular features. Conclusions: AI technologies could promote more accurate evidence-based management of EoE by integrating the results of molecular signature, clinical, histology, and endoscopic features. However, the era of AI application in medicine is just beginning; therefore, further studies with model validation in the real-world environment are required.
AMIKOMNET: Novel Structure for a Deep Learning Model to Enhance COVID-19 Classification Task Performance
Authors: Hanafi Muh
Published: July 2024

Since early 2020, coronavirus has spread extensively throughout the globe. It was first detected in Wuhan, a province in China. Many researchers have proposed various models to solve problems related to COVID-19 detection. As traditional medical approaches take a lot of time to detect the virus and require specific laboratory tests, the adoption of artificial intelligence (AI), including machine learning, might play an important role in handling the problem. A great deal of research has seen the adoption of AI succeed in the early detection of COVID-19 using X-ray images. Unfortunately, the majority of deep learning adoption for COVID-19 detection has the shortcomings of high error detection and high computation costs. In this study, we employed a hybrid model using an auto-encoder (AE) and a convolutional neural network (CNN) (named AMIKOMNET) with a small number of layers and parameters. We implemented an ensemble learning mechanism in the AMIKOMNET model using Adaboost with the aim of reducing error detection in COVID-19 classification tasks. The experimental results for the binary class show that our model achieved high effectiveness, with 96.90% accuracy, 95.06% recall, 94.67% F1-score, and 96.03% precision. The experimental result for the multiclass achieved 95.13% accuracy, 94.93% recall, 95.75% F1-score, and 96.19% precision. The adoption of Adaboost in AMIKOMNET for the binary class increased the effectiveness of the model to 98.45% accuracy, 96.16% recall, 95.70% F1-score, and 96.87% precision. The adoption of Adaboost in AMIKOMNET in the multiclass classification task also saw an increase in performance, with an accuracy of 96.65%, a recall of 94.93%, an F1-score of 95.76%, and a precision of 96.19%. The implementation of AE to handle image feature extraction combined with a CNN used to handle dimensional image feature reduction achieved outstanding performance when compared to previous work using a deep learning platform. Exploiting Adaboost also increased the effectiveness of the AMIKOMNET model in detecting COVID-19.
Reverse Vaccinology Approach to Identify Novel and Immunogenic Targets against Streptococcus gordonii
Authors: Abid Aneeqa , Alzahrani Badr , Naz Shumaila , Basheer Amina , Bakhtiar Syeda Marriam , Al-Asmari Fahad , Jamal Syed Babar , Faheem Muhammad
Published: July 2024

Streptococcus gordonii is a gram-positive, mutualistic bacterium found in the human body. It is found in the oral cavity, upper respiratory tract, and intestines, and presents a serious clinical problem because it can lead to opportunistic infections in individuals with weakened immune systems. Streptococci are the most prevalent inhabitants of oral microbial communities, and are typical oral commensals found in the human oral cavity. These streptococci, along with many other oral microbes, produce multispecies biofilms that can attach to salivary pellicle components and other oral bacteria via adhesin proteins expressed on the cell surface. Antibiotics are effective against this bacterium, but resistance against antibodies is increasing. Therefore, a more effective treatment is needed. Vaccines offer a promising method for preventing this issue. This study generated a multi-epitope vaccine against Streptococcus gordonii by targeting the completely sequenced proteomes of five strains. The vaccine targets are identified using a pangenome and subtractive proteomic approach. In the present study, 13 complete strains out of 91 strains of S. gordonii are selected. The pangenomics results revealed that out of 2835 pan genes, 1225 are core genes. Out of these 1225 core genes, 643 identified as non-homologous proteins by subtractive proteomics. A total of 20 essential proteins are predicted from non-homologous proteins. Among these 20 essential proteins, only five are identified as surface proteins. The vaccine construct is designed based on selected B- and T-cell epitopes of the antigenic proteins with the help of linkers and adjuvants. The designed vaccine is docked against TLR2. The expression of the protein is determined using in silico gene cloning. Findings concluded that Vaccine I with adjuvant shows higher interactions with TLR2, suggesting that the vaccine has the ability to induce a humoral and cell-mediated response to treat and prevent infection; this makes it promising as a vaccine against infectious diseases caused by S. gordonii. Furthermore, validation of the vaccine construct is required by in vitro and in vivo trials to check its actual potency and safety for use to prevent infectious diseases caused by S. gordonii.
Metabolic Profiling Changes Induced by Fermented Blackberries in High-Fat-Diet-Fed Mice Utilizing Gas Chromatography–Mass Spectrometry Analysis
Authors: Park Jae Young , Kim Ha-Rim , Lee Seung-Hyeon , Lee Sang-Wang , Sin Hong-Sig , Kim Seon-Young , Park Mi Hee
Published: July 2024

The aim of this study was to investigate the metabolic changes associated with the anti-obesity effects of fermented blackberry extracts in the liver tissues of high-fat-diet-fed mice using mass spectrometry-based metabolomics analysis. C57BL/6J mice were divided into eight groups: normal-diet-fed mice, high-fat-diet-fed mice, high-fat diet treated with blackberry extract, high-fat-diet mice treated with blackberry fermented by L. plantarum, and high-fat diet with blackberry fermented by L. brevis. After 12 weeks, the high-fat-diet group exhibited a greater increase in liver weight compared to the control group, and among the groups, the group administered with blackberry fermented with L. plantarum showed the most pronounced reduction in liver weight. As the primary organ responsible for amino acid metabolism, the liver is crucial for maintaining amino acid homeostasis. In our study, we observed that the levels of several essential amino acids, including isoleucine and valine, were decreased by the high-fat diet, and were recovered by administration of blackberry extract fermented with L. plantarum. Our results demonstrated the potential of blackberry extract fermented with L. plantarum as a functional material for metabolic disorders by restoring some of the amino acid metabolism disturbances induced by a high-fat diet.
Combined Metabolomic and NIRS Analyses Reveal Biochemical and Metabolite Changes in Goat Milk Kefir under Different Heat Treatments and Fermentation Times
Authors: Sánchez-Rodríguez Rubén , Terriente-Palacios Carlos , García-Olmo Juan , Osorio Sonia , Rodríguez-Ortega Manuel J.
Published: July 2024

Dairy products are an important source of protein and other nutrients in the Mediterranean diet. In these countries, the most common sources of milk for producing dairy products are cow, goat, sheep, and buffalo. Andalusia is traditionally the largest producer of goat milk in Spain. Kefir is a fermented product made from bacteria and yeasts and has health benefits beyond its nutritional properties. There is a lack of knowledge about the molecular mechanisms and metabolites that bring about these benefits. In this work, the combination of analytical techniques (GC-FID, UHPLC-MS-QToF, GC-QqQ-MS, and GC-ToF-MS) resulted in the detection of 105 metabolites in kefir produced with goat milk from two different thermal treatments (raw and pasteurized) fermented at four time points (12, 24, 36, and 48 h, using 0 h as the control). Of these, 27 metabolites differed between kefir produced with raw and pasteurized milk. These changes could possibly be caused by the effect of pasteurization on the microbial population in the starting milk. Some interesting molecules were identified, such as shikimic acid, dehydroabietic acid, GABA, and tyramine, which could be related to antibacterial properties, strengthening of the immune system, and arterial pressure. Moreover, a viability assay of the NIRS technique was performed to evaluate its use in monitoring the fermentation and classification of samples, which resulted in a 90% accuracy in comparison to correctly classified samples according to their fermentation time. This study represents the most comprehensive metabolomic analysis of goat milk kefir so far, revealing the intricate changes in metabolites during fermentation and the impact of milk treatment.
Chemogenetic Excitation of Ventromedial Hypothalamic Steroidogenic Factor 1 (SF1) Neurons Increases Muscle Thermogenesis in Mice
Authors: Watts Christina A. , Smith Jordan , Giacomino Roman , Walter Dinah , Jang Guensu , Malik Aalia , Harvey Nicholas , Novak Colleen M.
Published: July 2024

Allostatic adaptations to a perceived threat are crucial for survival and may tap into mechanisms serving the homeostatic control of energy balance. We previously established that exposure to predator odor (PO) in rats significantly increases skeletal muscle thermogenesis and energy expenditure (EE). Evidence highlights steroidogenic factor 1 (SF1) cells within the central and dorsomedial ventromedial hypothalamus (c/dmVMH) as a modulator of both energy homeostasis and defensive behavior. However, the brain mechanism driving elevated EE and muscle thermogenesis during PO exposure has yet to be elucidated. To assess the ability of SF1 neurons of the c/dmVMH to induce muscle thermogenesis, we used the combined technology of chemogenetics, transgenic mice, temperature transponders, and indirect calorimetry. Here, we evaluate EE and muscle thermogenesis in SF1-Cre mice exposed to PO (ferret odor) compared to transgenic and viral controls. We detected significant increases in muscle temperature, EE, and oxygen consumption following the chemogenetic stimulation of SF1 cells. However, there were no detectable changes in muscle temperature in response to PO in either the presence or absence of chemogenetic stimulation. While the specific role of the VMH SF1 cells in PO-induced thermogenesis remains uncertain, these data establish a supporting role for SF1 neurons in the induction of muscle thermogenesis and EE similar to what is seen after predator threats.
Concurrent Validity of Depth-Sensor-Based Quantification of Compensatory Movements during the Swing Phase of Gait in Healthy Individuals
Authors: Kusuda Kento , Matsubara Shigehito , Noguchi Daisuke , Kuwahara Moe , Hamasaki Hiroomi , Miwa Toshihiro , Maeda Toru , Nakanishi Toshihito , Ninomiya Shogo , Honda Keita
Published: July 2024

The advancement in depth-sensor technology increased the potential for the clinical use of markerless three-dimensional motion analysis (3DMA); however, the accurate quantification of depth-sensor-based 3DMA on gait characteristics deviating from normal patterns is unclear. This study investigated the concurrent validity of the measurements of compensatory movements measured by depth-sensor-based 3DMA compared to those measured by marker-based 3DMA. We induced swing-phase compensatory movements due to insufficient toe clearance by restricting unilateral ankle and knee joint movements in healthy individuals. Thirty-two healthy young adults (nineteen males, aged 20.4 ± 2.0 years, height 164.4 ± 9.8 cm, weight 60.0 ± 9.3 kg [average ± standard deviation]) walked the 6 m walkway in slow speed, very slow speed, and knee–ankle–foot orthosis (KAFO; participants wore KAFOs on the right leg) conditions. Gait kinematics were measured with marker-based and depth-sensor-based 3DMA systems. The intraclass correlation coefficient (ICC3,1) was used to measure the relative agreement between depth-sensor-based and marker-based 3DMA and demonstrated good or moderate validity for swing-phase compensatory movement measurement. Additionally, the ICC2,1 measured absolute agreement between the systems and showed lower validity than the ICC3,1. The measurement errors for contralateral vaulting, trunk lateral flexion, hip hiking, swing-side hip abduction, and circumduction between instruments were 0.01 m, 1.30°, 1.99°, 2.37°, and 1.53°, respectively. Depth-sensor-based 3DMA is useful for determining swing-phase compensatory movements, although the possibility of missing a slight measurement error of 1–2° must be considered.
Non-Invasive Brain Sensing Technologies for Modulation of Neurological Disorders
Authors: Alfihed Salman , Majrashi Majed , Ansary Muhammad , Alshamrani Naif , Albrahim Shahad H. , Alsolami Abdulrahman , Alamari Hala A. , Zaman Adnan , Almutairi Dhaifallah , Kurdi Abdulaziz , Alzaydi Mai M. , Tabbakh Thamer , Al-Otaibi Faisal
Published: July 2024

The non-invasive brain sensing modulation technology field is experiencing rapid development, with new techniques constantly emerging. This study delves into the field of non-invasive brain neuromodulation, a safer and potentially effective approach for treating a spectrum of neurological and psychiatric disorders. Unlike traditional deep brain stimulation (DBS) surgery, non-invasive techniques employ ultrasound, electrical currents, and electromagnetic field stimulation to stimulate the brain from outside the skull, thereby eliminating surgery risks and enhancing patient comfort. This study explores the mechanisms of various modalities, including transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS), highlighting their potential to address chronic pain, anxiety, Parkinson’s disease, and depression. We also probe into the concept of closed-loop neuromodulation, which personalizes stimulation based on real-time brain activity. While we acknowledge the limitations of current technologies, our study concludes by proposing future research avenues to advance this rapidly evolving field with its immense potential to revolutionize neurological and psychiatric care and lay the foundation for the continuing advancement of innovative non-invasive brain sensing technologies.
The Relationship between Molecular Structure and Foaming of Poly(ethylene glycol)—Poly(propylene glycol) Triblock Surfactants in Cementitious Materials
Authors: Tale Masoule Mohammad Sadegh , Ghahremaninezhad Ali
Published: July 2024

This study investigates the relationship between the molecular structure and foaming of poly(ethylene glycol) and poly(propylene glycol) triblock copolymers in Portland cement pastes. Four copolymers with different molecular structures were studied at varying concentrations. All copolymers showed a reduction in surface tension of the cement pore solution; however, only some of them demonstrated foaming and air entraining in cement paste. The results indicated that the molecular structure parameter, hydrophilic-to-lipophilic balance (HLB), has a direct relationship with the foaming and air-entraining performance of the copolymers. The total organic carbon measurements showed very small adsorption of these non-ionic copolymers on hydrating cement particles due to the lack of surface charge needed to interact with the heterogeneously charged surface of hydrating cement. In addition, these copolymers did not seem to affect the flow of cement paste due to a lack of adsorption on cement particles. The cement paste modified with the copolymers showed increased water sorption compared to the control paste due to the increased capillary porosity and slight increase in pore surface hydrophilicity. However, the freeze-thaw resistance was shown to improve with an increase in the number of air voids in the modified cement pastes. The findings establish the relationship between molecular properties of copolymers and their air-entraining performance in cement paste to mitigate the damages caused by freeze-thaw action.
LUNA EMG as a Marker of Adherence to Prehabilitation Programs and Its Effect on Postoperative Outcomes among Patients Undergoing Cytoreductive Surgery for Ovarian Cancer and Suspected Ovarian Tumors
Authors: Zębalski Marcin Adam , Parysek Krzysztof , Krzywon Aleksandra , Nowosielski Krzysztof
Published: July 2024

Background: Prehabilitation is a novel strategy in preoperative management. The aim of this study was to investigate the effect of prehabilitation programs on peri- and postoperative outcomes and to verify if LUNA EMG has the capacity to monitor compliance with prehabilitation programs. Methods: A total of seventy patients with suspected ovarian cancer were recruited between April 2021 and September 2022 and were divided into a prehabilitation group (36 patients) or a control group (34 patients). A LUNA EMG device was utilized to monitor muscle strength and tension. Results: Within the prehabilitation group, we observed a significant increase in the 6-Minute Walk Test distance by 17 m (median, IQR: 0–42.5, p < 0.001) and a significant increase in muscle strength measured with LUNA EMG. In comparison to the control group, the prehabilitation group showed fewer complications according to the Clavien–Dindo classification (47.2% vs. 20.6%, p = 0.02) and shorter postoperative hospital stays (median 5.0 days [IQR: 4.0–6.2] vs. 7.0 days [IQR: 6.0–10.0], p < 0.001). Conclusion: Prehabilitation has a positive effect on physical capacity and muscle strength and is associated with a reduction in the number of complications after surgery. LUNA EMG can be a useful tool for monitoring patients’ adherence to prehabilitation programs.
High M2-TAM Infiltration and STAT3/NF-κB Signaling Pathway as a Predictive Factor for Tumor Progression and Death in Cervical Cancer
Authors: Lira George , de Azevedo Fábio , Lins Ingrid , Marques Isabelle , Lira Giovanna , Eich Christina , de Araujo Junior Raimundo
Published: July 2024

Introduction: The tumor microenvironment (TME) plays a crucial role in the progression, invasion, and metastasis of cervical carcinoma (CC). Tumor-associated macrophages (TAMs) are significant components of the CC TME, but studies on their correlation with CC progression are still controversial. This study aimed to investigate the relationship between TAM infiltration, the STAT3/NF-κB signaling pathway, and Overall Survival (OS) in CC patients. Methods: In a retrospective study, 691 CC patients who had received a definitive histopathologic diagnosis of CC scored by the FIGO staging system and not undergone preoperative treatment were selected from a database. The effect of TAM infiltration on tumor progression biomarkers using Tissue Microarray (TMA) and immunohistochemistry was evaluated. Furthermore, the impact of the expression of these biomarkers and clinical–pathological parameters on recurrence-free (RF) and OS using Kaplan–Meier and multivariable Cox regression methods was also analyzed. Results: High stromal CD163 + 204 + TAMs density and via STAT3 and NF-κB pathways was relevant to the expression of E-cadherin, Vimentin, MMP9, VEGFα, Bcl-2, Ki-67, CD25, MIF, FOXP3, and IL-17 (all p < 0.0001). In addition, elevated TNM staging IV had a strong association correlation with STAT3 and NF-κB pathways (p < 0.0001), CD25 (p < 0.001), VEGFα (p < 0.001), MIF (p < 0.0001), and Ki-67 (p < 0.0001). On the other hand, overall and recurrence survival was shown to be strongly influenced by the expression of SNAIL (HR = 1.52), E-cadherin (HR = 1.78), and Ki-67 (HR = 1.44). Conclusion: M2-TAM and via STAT3/NF-κB pathways had a strong effect on CC tumor progression which reverberated in the severity of clinicopathological findings, becoming an important factor of poor prognosis.
Theoretical Study of Reversible Hydrogenation of CO2 to Formate Catalyzed by Ru(II)–PN5P, Fe(II)–PN5P, and Mn(I)–PN5P Complexes: The Effect of the Transition Metal Center
Authors: Meng Lingqiang , Yao Lihua , Li Jun
Published: July 2024

In 2022, Beller and coworkers achieved the reversible hydrogenation of CO2 to formic acid using a Mn(I)–PN5P complex with excellent activity and reusability of the catalyst. To understand the detailed mechanism for the reversible hydrogen release–storage process, especially the effects of the transition metal center in this process, we employed DFT calculations according to which Ru(II) and Fe(II) are considered as two alternatives to the Mn(I) center. Our computational results showed that the production of formic acid from CO2 hydrogenation is not thermodynamically favorable. The reversible hydrogen release–storage process actually occurs between CO2/H2 and formate rather than formic acid. Moreover, Mn(I) might not be a unique active metal for the reversible hydrogenation of CO2 to formate; Ru(II) would be a better option.
The Chick Chorioallantoic Membrane as a Xenograft Model for the Quantitative Analysis of Uveal Melanoma Metastasis in Multiple Organs
Authors: Liu Hongtao , Tsimpaki Theodora , Anastasova Ralitsa , Bechrakis Nikolaos E. , Fiorentzis Miltiadis , Berchner-Pfannschmidt Utta
Published: July 2024

Uveal melanoma (UM) is the most common intraocular tumor in adults, and nearly 50% of patients develop metastatic disease with a high mortality rate. Therefore, the development of relevant preclinical in vivo models that accurately recapitulate the metastatic cascade is crucial. We exploited the chick embryo chorioallantoic membrane (CAM) xenograft model to quantify both experimental and spontaneous metastasis by qPCR analysis. Our study found that the transplanted UM cells spread predominantly and early in the liver, reflecting the primary site of metastasis in patients. Visible signs of pigmented metastasis were observed in the eyes, liver, and distal CAM. Lung metastases occurred rarely and brain metastases progressed more slowly. However, UM cell types of different origins and genetic profiles caused an individual spectrum of organ metastases. Metastasis to multiple organs, including the liver, was often associated with risk factors such as high proliferation rate, hyperpigmentation, and epithelioid cell type. The severity of liver metastasis was related to the hepatic metastatic origin and chromosome 8 abnormalities rather than monosomy 3 and BAP1 deficiency. The presented CAM xenograft model may prove useful to study the metastatic potential of patients or to test individualized therapeutic options for metastasis in different organs.