bluuebunny/crossref_metadata_2025 huggingface dataset:
County Scale Corn Yield Estimation Based on Multi-source Data in Liaoning Province
Authors: Qu Ge , Shuai Yanmin , Shao Congying , Peng Xiuyuan , Huang Jiapeng
Published: May 2023

Corn as a dominant and productive cereal crop has been recognized as indispensable to the global food system and industrial raw materials. China’s corn consumption reached 2.82 × 108 t in 2021, but its production was only 2.65 × 108 t, and China’s corn industry is still in short supply. Timely and reliable corn yield estimation at a large scale is imperative and prerequisite to prevent climate risk and meet the growing demand for corn. While crop growth models are well suited to simulate yield formation, they lack the ability to provide fast and accurate estimates of large-scale yields, owing to the sheer quantity of data they require for parameterization. This study was conducted in the typical rain-fed corn belt, Liaoning province, to evaluate the applicability of our modeling practices. We developed the factors using climate data and MCD43A4 production, and built a county-level corn yield estimation model based on correlation analysis and corn growth mechanisms. We used corn yield data from the county between 2007 and 2017, leaving out 2017 for verification. The results show that our model, with an R2 (the Coefficient of Determination) of 0.82 and an RMSE (Root Mean Square Error) of 279.33 kg/hm2, significantly improved estimation accuracy compared to only using historical records and climate data. Our model’s R2 was 0.34 higher than the trend yield estimation model and 0.27 higher than the climate yield estimation model. Additionally, RMSE was reduced by 300–400 kg/hm2 compared to the other two models. The improvement in performance achieved by adding remote sensing information to the model was due to the inclusion of variables such as monitored corn growth state, which corrected the model predictions. Our work demonstrates a simple, scalable, and accurate method for timely estimation of corn yield at the county level with publicly available multiple-source data, which can potentially be employed in situations with sparse ground data for estimating crop yields.
Using Biostimulants, Soil Additives, and Plant Protectants to Improve Corn Yield in South Texas
Authors: Grichar W. James , Janak Travis W. , McGinty Joshua A. , Brewer Michael J.
Published: May 2023

Field studies were conducted in 2016, 2017, and 2020 in the south-central and Coastal Bend regions of Texas to determine the effects of various biostimulants, soil additives, and plant protectants on corn growth and yield. In south-central Texas, the use of pop-up fertilizer (9-30-0 + Zn) either alone or in combination with either 2% N, bifenthrin, or bifenthrin + pyraclostrobin resulted in the greatest corn vigor but a yield response was only noted with pop-up fertilizer alone at 28,062 or 46,771 mL ha−1 in one year. In the Coastal Bend region, leaf tissue analysis showed that only Fe was affected with the use of any soil additive. Bacillus licheniformis + Bacillus megaterium + Bacillus pumilus increased Fe leaf tissue content by 20% over the untreated check. Radicoat seed coating at 438 mL ha−1 reduced corn plant stand by 10%, and Pseudomonas brassicaceanum reduced corn height when compared with the untreated check; however, no differences in test weight or yield from the untreated check were noted with any soil additives. Little if any impacts of the use of biostimulants, soil amendments, or plant protectants were seen in these studies.
Antimicrobial Resistance of Escherichia coli Isolates from Livestock and the Environment in Extensive Smallholder Livestock Production Systems in Ethiopia
Authors: Gemeda Biruk Alemu , Wieland Barbara , Alemayehu Gezahegn , Knight-Jones Theodore J. D. , Wodajo Hiwot Desta , Tefera Misgana , Kumbe Adem , Olani Abebe , Abera Shubisa , Amenu Kebede
Published: May 2023

The objective of this study was to characterize the distribution of antimicrobial resistance (AMR) of Escherichia coli (E. coli) isolated from livestock feces and soil in smallholder livestock systems. A cross-sectional study was carried out sampling 77 randomly selected households in four districts representing two agroecologies and production systems. E. coli was isolated and the susceptibility to 15 antimicrobials was assessed. Of 462 E. coli isolates tested, resistance to at least one antimicrobial was detected in 52% (43.7–60.8) of isolates from cattle fecal samples, 34% (95% CI, 26.2–41.8) from sheep samples, 58% (95% CI, 47.9–68.2) from goat samples and 53% (95% CI, 43.2–62.4) from soil samples. AMR patterns for E. coli from livestock and soil showed some similarities, with the highest prevalence of resistance detected against streptomycin (33%), followed by amoxycillin/clavulanate (23%) and tetracycline (8%). The odds of detecting E. coli resistance to ≥2 antimicrobials in livestock fecal samples were nearly three times (Odd Ratio—OR: 2.9; 95% CI, 1.72–5.17; p = 0.000) higher in lowland pastoral than in highland mixed crop–livestock production systems. These findings provide insights into the status of resistance in livestock and soil, and associated risk factors in low-resource settings in Ethiopia.
BET Protein Inhibitor JQ1 Modulates Mitochondrial Dysfunction and Oxidative Stress Induced by Chronic Kidney Disease
Authors: Rayego-Mateos Sandra , Basantes Pamela , Morgado-Pascual José Luis , Brazal Prieto Beatriz , Suarez-Alvarez Beatriz , Ortiz Alberto , Lopez-Larrea Carlos , Ruiz-Ortega Marta
Published: May 2023

Among the mechanisms involved in the progression of kidney disease, mitochondrial dysfunction has special relevance. Epigenetic drugs such as inhibitors of extra-terminal domain proteins (iBET) have shown beneficial effects in experimental kidney disease, mainly by inhibiting proliferative and inflammatory responses. The impact of iBET on mitochondrial damage was explored in in vitro studies in renal cells stimulated with TGF-β1 and in vivo in murine unilateral ureteral obstruction (UUO) model of progressive kidney damage. In vitro, JQ1 pretreatment prevented the TGF-β1-induced downregulation of components of the oxidative phosphorylation chain (OXPHOS), such as cytochrome C and CV-ATP5a in human proximal tubular cells. In addition, JQ1 also prevented the altered mitochondrial dynamics by avoiding the increase in the DRP-1 fission factor. In UUO model, renal gene expression levels of cytochrome C and CV-ATP5a as well as protein levels of cytochrome C were reduced These changes were prevented by JQ1 administration. In addition, JQ1 decreased protein levels of the DRP1 fission protein and increased the OPA-1 fusion protein, restoring mitochondrial dynamics. Mitochondria also participate in the maintenance of redox balance. JQ1 restored the gene expression of antioxidant proteins, such as Catalase and Heme oxygenase 1 in TGF-β1-stimulated human proximal tubular cells and in murine obstructed kidneys. Indeed, in tubular cells, JQ1 decreased ROS production induced by stimulation with TGF-β1, as evaluated by MitoSOXTM. iBETs, such as JQ1, improve mitochondrial dynamics, functionality, and oxidative stress in kidney disease.
Pros and Cons of Dietary Vitamin A and Its Precursors in Poultry Health and Production: A Comprehensive Review
Authors: Khan Rifat Ullah , Khan Aamir , Naz Shabana , Ullah Qudrat , Puvača Nikola , Laudadio Vito , Mazzei Domenico , Seidavi Alireza , Ayasan Tugay , Tufarelli Vincenzo
Published: May 2023

Vitamin A is a fat-soluble vitamin that cannot be synthesized in the body and must be obtained through diet. Despite being one of the earliest vitamins identified, a complete range of biological actions is still unknown. Carotenoids are a category of roughly 600 chemicals that are structurally related to vitamin A. Vitamin A can be present in the body in the form of retinol, retinal, and retinoic acid. Vitamins are required in minute amounts, yet they are critical for health, maintenance, and performing key biological functions in the body, such as growth, embryo development, epithelial cell differentiation, and immune function. Vitamin A deficiency induces a variety of problems, including lack of appetite, decreased development and immunity, and susceptibility to many diseases. Dietary preformed vitamin A, provitamin A, and several classes of carotenoids can be used to meet vitamin A requirements. The aim of this review is to compile the available scientific literature regarding the sources and important functions, such as growth, immunity, antioxidant, and other biological activities of vitamin A in poultry.
Plasma Lipidomics Profiles Highlight the Associations of the Dual Antioxidant/Pro-oxidant Molecules Sphingomyelin and Phosphatidylcholine with Subclinical Atherosclerosis in Patients with Type 1 Diabetes
Authors: Sojo Lidia , Santos-González Elena , Riera Lídia , Aguilera Alex , Barahona Rebeca , Pellicer Paula , Buxó Maria , Mayneris-Perxachs Jordi , Fernandez-Balsells Mercè , Fernández-Real José-Manuel
Published: May 2023

Here, we report on our study of plasma lipidomics profiles of patients with type 1 diabetes (T1DM) and explore potential associations. One hundred and seven patients with T1DM were consecutively recruited. Ultrasound imaging of peripheral arteries was performed using a high image resolution B-mode ultrasound system. Untargeted lipidomics analysis was performed using UHPLC coupled to qTOF/MS. The associations were evaluated using machine learning algorithms. SM(32:2) and ether lipid species (PC(O-30:1)/PC(P-30:0)) were significantly and positively associated with subclinical atherosclerosis (SA). This association was further confirmed in patients with overweight/obesity (specifically with SM(40:2)). A negative association between SA and lysophosphatidylcholine species was found among lean subjects. Phosphatidylcholines (PC(40:6) and PC(36:6)) and cholesterol esters (ChoE(20:5)) were associated positively with intima-media thickness both in subjects with and without overweight/obesity. In summary, the plasma antioxidant molecules SM and PC differed according to the presence of SA and/or overweight status in patients with T1DM. This is the first study showing the associations in T1DM, and the findings may be useful in the targeting of a personalized approach aimed at preventing cardiovascular disease in these patients.
Discrimination of Green Coffee (Coffea arabica and Coffea canephora) of Different Geographical Origin Based on Antioxidant Activity, High-Throughput Metabolomics, and DNA RFLP Fingerprinting
Authors: Mannino Giuseppe , Kunz Ronja , Maffei Massimo E.
Published: May 2023

The genus Coffea is known for the two species C. arabica (CA) and C. canephora (CC), which are used to prepare the beverage coffee. Proper identification of green beans of coffee varieties is based on phenotypic and phytochemical/molecular characteristics. In this work, a combination of chemical (UV/Vis, HPLC-DAD–MS/MS, GC–MS, and GC-FID) and molecular (PCR-RFLP) fingerprinting was used to discriminate commercial green coffee accessions from different geographical origin. The highest content of polyphenols and flavonoids was always found in CC accessions, whereas CA showed lower values. ABTS and FRAP assays showed a significant correlation between phenolic content and antioxidant activity in most CC accessions. We identified 32 different compounds, including 28 flavonoids and four N-containing compounds. The highest contents of caffeine and melatonin were detected in CC accessions, whereas the highest levels of quercetin and kaempferol derivatives were found in CA accessions. Fatty acids of CC accessions were characterized by low levels of linoleic and cis octadecenoic acid and high amounts of elaidic acid and myristic acid. Discrimination of species according to their geographical origin was achieved using high-throughput data analysis, combining all measured parameters. Lastly, PCR-RFLP analysis was instrumental for the identification of recognition markers for the majority of accessions. Using the restriction enzyme AluI on the trnL-trnF region, we clearly discriminated C. canephora from C. arabica, whereas the cleavage performed by the restriction enzymes MseI and XholI on the 5S-rRNA-NTS region produced specific discrimination patterns useful for the correct identification of the different coffee accessions. This work extends our previous studies and provides new information on the complete flavonoid profile, combining high-throughput data with DNA fingerprinting to assess the geographical discrimination of green coffee.
Overview of Predictive Control Technology for Permanent Magnet Synchronous Motor Systems
Authors: Peng Jingyao , Yao Ming
Published: May 2023

Permanent magnet synchronous motors (PMSMs) are commonly used in the automation industry. With the speedy development of digital system processors, predictive control as a modern control scheme has been applied to improve the dynamic performance and work efficiency of PMSMs. This paper provides an overview of the research status of PMSM-based predictive control strategies. The deficiencies of the three most popular predictive schemes, deadbeat predictive control, finite-control-set model predictive control, and continuous-control-set model predictive control, and existing improvement strategies such as delay compensation schemes, robust control schemes, and multi-vector control schemes, are summarized. Finally, current technological trends are discussed, emphasizing future research directions for predictive control in PMSM drive systems.
A Cascading Failure Model of the Air Traffic Control Network Based on the Theory of Interdependent Networks
Authors: Bai Jie , Wang Buhong , Zeng Leya , Yang Yong
Published: May 2023

With the continuous development of information technology, a spontaneous interdependent network has formed within the air traffic control network. Due to the internal interdependence, any small, failed node may trigger a cascade failure of the entire system. The purpose of this study is to investigate the resilience of air traffic control networks. Based on air traffic management regulations, a new cascading failure model for air traffic control networks is proposed, which is based on the theory of interdependent networks. The model establishes a dual-layer dependency relationship between the control coordination network and the air route facility network, including control dependency and service dependency. Through experiments, targeted measures are proposed to improve the safety and reliability of air traffic control. This model introduces parameters such as control cost and node control capability, and reflects the resilience of the air traffic control network, based on the final number of failed nodes after the steady-state of the cascade failure, the network’s cascade failure rate, and the system’s load failure threshold. Simulation results show that enhancing the control capability and increasing the number of control positions can improve the control cost of the air traffic control network. The higher the control cost, the better the resilience of the air traffic control network. Improving the control capability of control nodes has a greater impact on the resilience of the air traffic control network, compared to increasing the number of control nodes. The degree attack on route nodes has a greater impact on the cascade failure of the air traffic control network, compared to random attacks and facility node degree attacks. The cascade failure model proposed in this paper provides a new method for guiding the air traffic control network to resist cascade failure attacks and enhance its resilience.
Optimization of the Join between Large Tables in the Spark Distributed Framework
Authors: Wu Xiang , He Yueshun
Published: May 2023

The Join task between Spark large tables takes a long time to run and produces a lot of disk I/O, network I/O and disk occupation in the Shuffle process. This paper proposes a lightweight distributed data filtering model that combines broadcast variables and accumulators using RoaringBitmap. When the data in the two tables are not exactly matched, the dimension table Key is collected through the accumulator, compressed by RoaringBitmap and distributed to each node using broadcast variables. The distributed fact table data can be pre-filtered on the local server, which effectively reduces the data transmission and disk reading and writing in the Shuffle phase. Experimental results show that this optimization method can reduce disk usage, shorten the running time and reduce network I/O and disk I/O for Spark Join tasks in the case of massive data, and the effect is more obvious when the two tables have a higher incomplete matching degree or a fixed matching degree but a larger amount of data. This optimization scheme has the advantages of being easy to use, being easy to maintain and having an obvious effect, and it can be applied to many development scenarios.
Design and Functionality of a Prototype for Cold Needle Perforation of Wheat
Authors: Stäheli Luca , Schwab Lisa , Kinner Mathias , Müller Nadina
Published: May 2023

Wheat is a vital crop in global food security, but up to 25% of the wheat harvested is contaminated with mycotoxins that have detrimental effects on human health. To address this issue, biological detoxification strategies have been developed using microorganisms and enzymes. Perforating the whole wheat kernel using cold needle perforation (CNP) followed by a detoxification step could be a promising approach to reduce cross-contamination during the milling of mycotoxin-containing wheat. In this study, a pilot-scale CNP prototype was developed to perforate wheat kernels, and its effectiveness was evaluated. The height-adjustable perforation unit consists of 3120 needles. The throughput of the CNP prototype was adjusted to 6 kg/h, and the kernels were perforated for 1, 5, or 10 cycles. The results show that the CNP prototype effectively perforates wheat kernels, as evidenced by the significant increase in pore count. Fluorescence microscopy confirmed the penetration of particles in the size range of enzymes and microorganisms into the kernel. This study demonstrates the successful scale-up of CNP for wheat kernel perforation and highlights the potential of CNP as a cost-effective and efficient method for the biological detoxification of mycotoxin-contaminated wheat.
Effect of Laser Treatment on Intrinsic Mechanical Stresses in Titanium and Some of Its Alloys
Authors: Jażdżewska Magdalena , Bartmański Michał , Zieliński Andrzej , Kwidzińska Dominika Beata
Published: May 2023

Laser surface treatment conducted at different power levels is an option to modify titanium bone implants to produce nano- and microtopography. However, such processing can lead to excess mechanical stress within the surface layer. This research aims to calculate the level of such residual stresses after the surface processing of Ti grade IV, Ti15Mo, and Ti6Al7Nb alloys with an Nd:YAG laser. Light and scanning electron microscopies (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffractometry (XRD), optical profilography, and nano-indentation tests were applied to characterize the surface zone. The laser processing resulted in a distinct surface pattern and the formation of remelted zones 66–126 µm thick, with roughness values ranging between 0.22 and 1.68 µm. The mechanical properties were weakly dependent on the material composition. The residual stresses caused by the laser treatment were moderate, always tensile, increasing with loading, and was the highest for the Ti15Mo alloy.
Effect of Sodium Stearate on the Microstructure and Hydration Process of Alkali-Activated Material
Authors: Yao Kai , Li Qing , Chen Ping , Li Juntong , Wang Penghuai , Tang Wenqi
Published: May 2023

In response to the high water sorptivity of alkali-activated slag–red mud–steel slag cementitious material (AASRSS), water and aggressive ions are easily transferred to the interior of cementitious materials, which poses a significant threat to their durability. In order to limit the transfer of moisture and aggressive ions to the interior of AASRSS, sodium stearate (NaSt) was utilized in this paper to reduce its water sorptivity. The aim of this study was to elucidate the role of NaSt in the AASRSS system. The changes in the compressive strength and water sorptivity of AASRSS mortar were tested by mixing different amounts of NaSt into the mortar. The changes in the hydration process and microstructure of AASRSS after mixing with NaSt were analyzed by using the test methods of the isothermal calorimetry, resistivity, SEM and MIP. The results showed that NaSt plays an important role in the pore structure characteristics of AASRSS and that the use of NaSt significantly reduces its water sorptivity. The improvement in the water sorptivity was caused by two main mechanisms, namely the optimization of the pore structure (reduction in defects of the microstructure and alteration of pore size distribution) and the introduction of a hydrophobic film on the pore surface. The addition of NaSt did not change the type of AASRSS hydration product, but it inhibited the hydration reaction, leading to a reduction in the hydration product. The combination of the increased porosity and reduced hydration products mainly accounts for the decrease in the compressive strength of AASRSS due to NaSt.
Analysis and Design of Nonlinear Tuned Mass Damper Based on Complex Variable Averaging Method
Authors: Hu Yujun , Yao Ji , Liu Junfeng , Zhang Qing
Published: May 2023

As one of the most representative passive control devices, tuned mass dampers (TMDs) are widely used in civil engineering, aerospace, machinery, and other fields, after years of research and improvement. However, due to their large displacement and the use of a limiting device, they inevitably exhibit some nonlinear characteristics in practical engineering applications. This nonlinearity is often ignored; however, neglecting it in the design process can adversely affect the control performance. Therefore, considering the nonlinearity of a TMD while designing TMD parameters can make the calculation results closer to reality and benefit the structural design. In this paper, we derived the approximate analytical solution of TMD amplitude using the complex variable averaging method by considering the nonlinearity generated by a TMD in the vibration process. Theoretical optimal design parameters were obtained by analytical comparison, and we compared the computational time consumption of this method and the numerical method. The results showed that the optimized parameters of the TMD obtained by nonlinear design possessed a good vibration reduction effect both before and after the TMD generated nonlinear characteristics. Additionally, the complex averaging method generated frequency response curves tens of times faster than the numerical method.
Estimation of Age in Humans Using Dental Translucency of Permanent Teeth: An Experimental Study
Authors: Rinaldo Natascia , Saguto Ilaria , De Luca Federica , Neri Margherita , Frisoni Paolo , Gualdi-Russo Emanuela
Published: May 2023

In forensics, the positive identification of decomposed or skeletonized bodies is a fundamental task, with the age-at-death estimation of adult individuals as one of the main objectives. Among different dental methods, root dentin translucency (RDT) is often applied since it is easy to perform and non-destructive. However, this method has some biases, and several equations have been proposed in the literature. This study aimed to test the performance of the previously published equations in an Italian sample with known age and sex, and to develop an equation specific to the Italian population. In total, we examined a sample of 155 single and multi-rooted teeth from male and female individuals aged 18-85 years. The regression equation developed for Italians was tested on a holdout sample drawn from the same population. Intra- and inter-observer errors were calculated using ICC analysis. Both root length and RDT showed excellent repeatability and reproducibility regardless of tooth type. Two of the seven published equations tested performed better in our sample, but the newly proposed equation performed better than those on the Italian population. In conclusion, RDT has proven to be a reliable indicator for age estimation, and the proposed new formula may be effective in such estimation, especially in individuals aged <40.
Spatial–Temporal Correlation Considering Environmental Factor Fusion for Estimating Gross Primary Productivity in Tibetan Grasslands
Authors: Yang Qinmeng , Nie Ningming , Wang Yangang , Wu Xiaojing , Liu Weihua , Ren Xiaoli , Wang Zijian , Wan Meng , Cao Rongqiang
Published: May 2023

Gross primary productivity (GPP) is an important indicator in research on carbon cycling in terrestrial ecosystems. High-accuracy GPP prediction is crucial for ecosystem health and climate change assessments. We developed a site-level GPP prediction method based on the GeoMAN model, which was able to extract spatiotemporal features and fuse external environmental factors to predict GPP on the Tibetan Plateau. We evaluated four models’ behavior—Random Forest (RF), Support Vector Machine (SVM), Deep Belief Network (DBN), and GeoMAN—in predicting GPP at nine flux observation sites on the Tibetan Plateau. The GeoMAN model achieved the best results (R2 = 0.870, RMSE = 0.788 g Cm−2 d−1, MAE = 0.440 g Cm−2 d−1). Distance and vegetation type of the flux sites influenced GPP prediction, with the latter being more significant. The different grassland vegetation types exhibited different sensitivity to environmental factors (Ta, PAR, EVI, NDVI, and LSWI) for GPP prediction. Among them, the site located in the alpine swamp meadow was insensitive to changes in environmental factors; the GPP prediction accuracy of the site located in the alpine meadow steppe decreased significantly with the changes in environmental factors; and the GPP prediction accuracy of the site located in the alpine Kobresia meadow also varied with environmental factor changes, but to a lesser extent than the former. This study provides a good reference that deep learning model is able to achieve good accuracy in GPP simulation when considers spatial, temporal, and environmental factors, and the judgement made by deep learning model conforms to basic knowledge in the relevant field.
Assessment of Bone Mineral Density from Lumbosacral MRI: A Retrospective Study with Texture Analysis Radiomics
Authors: Vara Giulio , Spinnato Paolo , Facchini Giancarlo , Miceli Marco , Ursini Francesco , Spinardi Luca , Vornetti Gianfranco , Ratti Stefano
Published: May 2023

Osteoporosis is a common condition characterized by low bone mineral density (BMD) and deterioration of bone microarchitecture, leading to increased risk of fractures. Early diagnosis and treatment of osteoporosis are critical for preventing fractures and their associated morbidity and mortality. Currently, dual-energy X-ray absorptiometry (DXA) is the gold standard for assessing BMD; however, it has limitations such as radiation exposure, cost, and limited availability in certain regions. Magnetic resonance imaging (MRI) of the lumbar spine is routinely performed for various indications, and it provides high-resolution images of the bone and tissue without ionizing radiation. Recently, texture analysis (TA) of MRI images has shown promise in assessing BMD by quantifying the spatial distribution and heterogeneity of bone marrow fat and trabecular bone. In this article, we present our experience with the opportunistic use of lumbar spine MRI for BMD assessment using TA, and we compare the results with DXA measurements. We also discuss the potential clinical implications of this approach, including its use in patients who cannot undergo DXA or in whom BMD assessment is not routinely performed. MRI should provide information in a single examination in regard to degenerative disk pathology and arthritis, with the addition of BMD prediction.
Advanced Elastic and Reservoir Properties Prediction through Generative Adversarial Network
Authors: Ishak Muhammad Anwar , Abdul Latiff Abdul Halim , Ho Eric Tatt Wei , Fuad Muhammad Izzuljad Ahmad , Tan Nian Wei , Sajid Muhammad , Elsebakhi Emad
Published: May 2023

The prediction of subsurface properties such as velocity, density, porosity, and water saturation has been the main focus of petroleum geosciences. Advanced methods such as Full Waveform Inversion (FWI), Joint Migration Inversion (JMI) and ML-Rock Physics are able to produce better predictions than their predecessors, but they still require tedious manual interpretation that is prone to human error. The research on these methods remains open as they suffer from technical limitations. As computing resources are becoming cheaper, the use of a single deep-generative adversarial network is feasible in predicting all these properties in a completely data-driven manner. In our proposed method of multiscale pix2pix applied to SEG SEAM salt data, we have managed to map from one input, which is seismic post-stack data, to several outputs of reservoir and elastic properties such as porosity, velocity, and density by using only one trained model and without having to manually interpret or pre-process the input data. With 90% accuracy of the results in the synthetic data testing, the method is worthy of being explored by the petroleum geoscience fraternity.
Analysis of Data on Air Pollutants in the City by Machine-Intelligent Methods Considering Climatic and Geographical Features
Authors: Temirbekov Nurlan , Kasenov Syrym , Berkinbayev Galym , Temirbekov Almas , Tamabay Dinara , Temirbekova Marzhan
Published: May 2023

In the world, air pollution ranks among the primary sources of risk to human health and the environment. To assess the risk of impact of atmospheric pollution, a comprehensive research cycle was designed to develop a unified ecosystem for monitoring air pollution in industrial cities in Kazakhstan. Research involves analyzing data for the winter period from 20 automated monitoring stations (AMS) located in Almaty and conducting chemical-analytical studies of snowmelt water samples from 22 points to identify such pollutants as fine particulate matters, petroleum products, and heavy metals. Research includes a bio-experiment involving the cultivation of watercress on samples of melt water collected from snow cover to examine the effects of pollution on plants. In the framework of this research, we determined API based on data obtained from AMS. In order to determine the influence of atmospheric pollution on the environment, a multiple regression model was developed using machine learning algorithms to reveal the relationship between the bio-experiment data and data on pollutants of chemical-analytical research. The results revealed a wide spread of pollutants in the snow cover of the urban environment, a correlation between pollutants in the snow cover and the airspace of the city, and their negative impact on flora.
Inverse Unit Teissier Distribution: Theory and Practical Examples
Authors: Alsadat Najwan , Elgarhy Mohammed , Karakaya Kadir , Gemeay Ahmed M. , Chesneau Christophe , Abd El-Raouf M. M.
Published: May 2023

In this paper, we emphasize a new one-parameter distribution with support as [1,+∞). It is constructed from the inverse method applied to an understudied one-parameter unit distribution, the unit Teissier distribution. Some properties are investigated, such as the mode, quantiles, stochastic dominance, heavy-tailed nature, moments, etc. Among the strengths of the distribution are the following: (i) the closed-form expressions and flexibility of the main functions, and in particular, the probability density function is unimodal and the hazard rate function is increasing or unimodal; (ii) the manageability of the moments; and, more importantly, (iii) it provides a real alternative to the famous Pareto distribution, also with support as [1,+∞). Indeed, the proposed distribution has different functionalities but also benefits from the heavy-right-tailed nature, which is demanded in many applied fields (finance, the actuarial field, quality control, medicine, etc.). Furthermore, it can be used quite efficiently in a statistical setting. To support this claim, the maximum likelihood, Anderson–Darling, right-tailed Anderson–Darling, left-tailed Anderson–Darling, Cramér–Von Mises, least squares, weighted least-squares, maximum product of spacing, minimum spacing absolute distance, and minimum spacing absolute-log distance estimation methods are examined to estimate the unknown unique parameter. A Monte Carlo simulation is used to compare the performance of the obtained estimates. Additionally, the Bayesian estimation method using an informative gamma prior distribution under the squared error loss function is discussed. Data on the COVID mortality rate and the timing of pain relief after receiving an analgesic are considered to illustrate the applicability of the proposed distribution. Favorable results are highlighted, supporting the importance of the findings.
Human Bone Marrow-Derived Mesenchymal Stem Cell Applications in Neurodegenerative Disease Treatment and Integrated Omics Analysis for Successful Stem Cell Therapy
Authors: Kim Seok Gi , George Nimisha Pradeep , Hwang Ji Su , Park Seokho , Kim Myeong Ok , Lee Soo Hwan , Lee Gwang
Published: May 2023

Neurodegenerative diseases (NDDs), which are chronic and progressive diseases, are a growing health concern. Among the therapeutic methods, stem-cell-based therapy is an attractive approach to NDD treatment owing to stem cells’ characteristics such as their angiogenic ability, anti-inflammatory, paracrine, and anti-apoptotic effects, and homing ability to the damaged brain region. Human bone-marrow-derived mesenchymal stem cells (hBM-MSCs) are attractive NDD therapeutic agents owing to their widespread availability, easy attainability and in vitro manipulation and the lack of ethical issues. Ex vivo hBM-MSC expansion before transplantation is essential because of the low cell numbers in bone marrow aspirates. However, hBM-MSC quality decreases over time after detachment from culture dishes, and the ability of hBM-MSCs to differentiate after detachment from culture dishes remains poorly understood. Conventional analysis of hBM-MSCs characteristics before transplantation into the brain has several limitations. However, omics analyses provide more comprehensive molecular profiling of multifactorial biological systems. Omics and machine learning approaches can handle big data and provide more detailed characterization of hBM-MSCs. Here, we provide a brief review on the application of hBM-MSCs in the treatment of NDDs and an overview of integrated omics analysis of the quality and differentiation ability of hBM-MSCs detached from culture dishes for successful stem cell therapy.
Customized Multichannel Measurement System for Microbial Fuel Cell Characterization
Authors: Lovecchio Nicola , Di Meo Valentina , Pietrelli Andrea
Published: May 2023

This work presents the development of an automatic and customized measuring system employing sigma-delta analog-to-digital converters and transimpedance amplifiers for precise measurements of voltage and current signals generated by microbial fuel cells (MFCs). The system can perform multi-step discharge protocols to accurately measure the power output of MFCs, and has been calibrated to ensure high precision and low noise measurements. One of the key features of the proposed measuring system is its ability to conduct long-term measurements with variable time steps. Moreover, it is portable and cost-effective, making it ideal for use in laboratories without sophisticated bench instrumentation. The system is expandable, ranging from 2 to 12 channels by adding dual-channel boards, which allows for testing of multiple MFCs simultaneously. The functionality of the system was tested using a six-channel setup, and the results demonstrated its ability to detect and distinguish current signals from different MFCs with varying output characteristics. The power measurements obtained using the system also allow for the determination of the output resistance of the MFCs being tested. Overall, the developed measuring system is a useful tool for characterizing the performance of MFCs, and can be helpful in the optimization and development of sustainable energy production technologies.
Novel Technologies for Exosome and Exosome-like Nanovesicle Procurement and Enhancement
Authors: Martínez-Santillán Andrés , González-Valdez José
Published: May 2023

Exosomes are extracellular nanovesicles commonly produced by mammalian cells that in recent years have risen as a novel strategy for drug delivery systems and cancer therapy because of their innate specificity and high bioavailability. However, there are limitations that undermine their potential. Among them is the lack of mass production capacity with the current available sources and the failure to reach the intended therapeutic effect because of their insufficient uptake or their rapid clearance once administered. This review aims to show the current advances in overcoming these limitations by presenting, firstly, reported strategies to improve exosome and exosome-like nanovesicle extraction from possible novel eukaryotic sources, including animals, plants, and protozoa; and secondly, alternative modification methods that functionalize exosomes by conferring them higher targeting capacity and protection from organism defenses, which results in an increase in the attachment of ligands and cellular uptake of inorganic materials. However, even when these strategies might address some of the obstacles in their procurement and therapeutic use, there are still several aspects that need to be addressed, so several perspectives of the matter are also presented and analyzed throughout this work.
Decreased MARCKS Protein Expression in Kidney Cortex Membrane Fractions of Cathepsin B Knockout Mice Is Associated with Reduced Lysophosphatidylcholine and Protein Kinase C Activity
Authors: Kawakibi Tamim , Bala Niharika , Liu Lauren P. , Searcy Louis A. , Denslow Nancy D. , Alli Abdel A.
Published: May 2023

Cathpesin B is a multi-functional protease that plays numerous roles in physiology and pathophysiology. We hypothesized that actin cytoskeleton proteins that are substrates of cathepsin B, various lipids, and kinases that are regulated by lipids would be down-regulated in the kidney of cathepsin B knockout mice. Here, we show by Western blot and densitometric analysis that the expression and proteolysis of the actin cytoskeleton proteins myristoylated alanine-rich C-kinase substrate (MARCKS) and spectrin are significantly reduced in kidney cortex membrane fractions of cathepsin B knockout mice compared to C57B6 wild-type control mice. Lipidomic results show that specific lipids are increased while other lipids, including lysophosphatidylcholine (LPC) species LPC (16:0), LPC (18:0), LPC (18:1), and LPC (18:2), are significantly decreased in membrane fractions of the kidney cortex from Cathepsin B null mice. Protein Kinase C (PKC) activity is significantly lower in the kidney cortex of cathepsin B knockout mice compared to wild-type mice, while calcium/calmodulin-dependent protein kinase II (CaMKII) activity and phospholipase D (PLD) activity are comparable between the two groups. Together, these results provide the first evidence of altered actin cytoskeleton organization, membrane lipid composition, and PKC activity in the kidneys of mice lacking cathepsin B.
Application of Chlorella vulgaris Beijerinck as a Biostimulant for Growing Cucumber Seedlings in Hydroponics
Authors: Vildanova Galiya I. , Allaguvatova Rezeda Z. , Kunsbaeva Dina F. , Sukhanova Natalia V. , Gaysina Lira A.
Published: May 2023

Hydroponics is a promising method for growing agricultural plants and is especially relevant in the context of global climate change. Microscopic algae, including Chlorella vulgaris, have great potential for use in hydroponic systems as natural growth stimulators. The effect of the suspension of an authentic strain of Chlorella vulgaris Beijerinck on the length of cucumber shoots and roots, as well as its dry biomass, was studied. During cultivation in a Knop medium with the addition of Chlorella suspension, the length of the shoots was shortened from 11.30 to 8.15 cm, while the length of the roots also decreased from 16.41 to 10.59 cm. At the same time, the biomass of the roots increased from 0.04 to 0.05 g. The data obtained indicate the positive effect of the suspension of the Chlorella vulgaris authentic strain on the dry biomass of cucumber plants in hydroponic conditions and make it possible to recommend this strain for use when growing plants in hydroponic systems.
Assessment of Lumbosacral Nerve Roots in Patients with Type 2 Diabetic Peripheral Neuropathy Using Diffusion Tensor Imaging
Authors: Chen He , Xu Yanyan , Wang Wei , Deng Ruifen , Li Zhaoqing , Xie Sheng , Jiao Jinsong
Published: May 2023

Background: Diffusion tensor imaging (DTI) has found clinical applications in the evaluation of the central nervous system and has been extensively used to image peripheral neuropathy. However, few studies have focused on lumbosacral nerve root fiber damage in diabetic peripheral neuropathy (DPN). The aim of the study was to evaluate whether DTI of the lumbosacral nerve roots can be used to detect DPN. Methods: Thirty-two type 2 diabetic patients with DPN and thirty healthy controls (HCs) were investigated with a 3T MRI scanner. DTI with tractography of the L4, L5, and S1 nerve roots was performed. Anatomical fusion with the axial T2 sequences was used to provide correlating anatomical information. Mean fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were measured from tractography images and compared between groups. Diagnostic value was assessed using receiver operating characteristic (ROC) analysis. The Pearson correlation coefficient was used to explore the correlation between DTI parameters and clinical data and the nerve conduction study (NCS) in the DPN group. Results: In the DPN group, FA was decreased (p < 0.001) and ADC was increased (p < 0.001) compared with the values of the HC group. FA displayed the best diagnostic accuracy, with an area under the ROC curve of 0.716. ADC was positively correlated with HbA1c level (r = 0.379, p = 0.024) in the DPN group. Conclusions: DTI of lumbosacral nerve roots demonstrates appreciable diagnostic accuracy in patients with DPN.
Brain State Relays Self-Processing and Heartbeat-Evoked Cortical Responses
Authors: Huang Ying , Xie Musi , Liu Yunhe , Zhang Xinyu , Jiang Liubei , Bao Han , Qin Pengmin , Han Junrong
Published: May 2023

The self has been proposed to be grounded in interoceptive processing, with heartbeat-evoked cortical activity as a neurophysiological marker of this processing. However, inconsistent findings have been reported on the relationship between heartbeat-evoked cortical responses and self-processing (including exteroceptive- and mental-self-processing). In this review, we examine previous research on the association between self-processing and heartbeat-evoked cortical responses and highlight the divergent temporal-spatial characteristics and brain regions involved. We propose that the brain state relays the interaction between self-processing and heartbeat-evoked cortical responses and thus accounts for the inconsistency. The brain state, spontaneous brain activity which highly and continuously changes in a nonrandom way, serves as the foundation upon which the brain functions and was proposed as a point in an extremely high-dimensional space. To elucidate our assumption, we provide reviews on the interactions between dimensions of brain state with both self-processing and heartbeat-evoked cortical responses. These interactions suggest the relay of self-processing and heartbeat-evoked cortical responses by brain state. Finally, we discuss possible approaches to investigate whether and how the brain state impacts the self-heart interaction.
Satisfaction among the Elderly Living in the Ancient Town of Xiangxi
Authors: Zhang Fupeng , Shi Lei , Liu Simian , Cheng Mengfei , Zeng Fanxuan , Li Jin
Published: May 2023

This exploratory study combines the elderly-friendly post-use evaluation theory with the specific context of traditional residential buildings in typical Tujia ancient towns in the Western Hunan region, namely, Furong, Liexi, Xichehe, and Liye. A post-use low satisfaction evaluation model of traditional residential buildings in ancient towns was constructed, and the importance ranking of evaluation factors, along with the importance analysis of secondary indicators, was discussed using statistical methods. The subjective evaluations of elderly residents in different ancient towns toward the living environment and the importance ranking of factors for low satisfaction were examined. The findings of this study showed that we should focus on safety, convenience, health, privacy, belonging and cultural factors, and factors influencing sustainable livability for improving the quality of life of elderly residents in Tujia ancient towns in Western Hunan. Finally, the study proposes targeted improvement strategies to guide the construction of elderly-friendly environments in Tujia ancient towns and provide a reference for low satisfaction evaluations of ancient towns in similar economically underdeveloped areas.
A 3D Spatial Diagnostic Framework of Sustainable Historic and Cultural District Preservation: A Case Study in Henan, China
Authors: Zhang Man , Zhang Yaoxin , Fang Xue , Wang Xiaoqi
Published: May 2023

Three-dimensional (3D) information technology has become an important technical support in digital heritage preservation practice. However, due to the lack of systematic quantitative research, it is difficult to form a comprehensive understanding of the historic and cultural districts, from macro to micro. Our study aimed to establish a systematic 3D spatial diagnostic framework combining 3D scanning and SPSS data descriptive analysis and regression analysis for historic and cultural districts to promote sustainable historic and cultural area preservation. Taking Zhongshan Street in Qi County as an example, data statistical analysis was carried out on morphological feature data from the macro level of the district, the meso level of architecture, and the micro level of elements. The research conclusion shows that at the macro level the street form continues the main features of a traditional alley spatial skyline, height–width ratio, and sectional symbol language. At the meso level, the architecture reveals various periods of style in terms of the facade width and mathematical relationship between traditional architectural facades. At the micro level, architectural detailing explains the main reasons for the recent new construction being inconsistent with the historic and cultural district appearance. This quantitative diagnostic method can accurately analyze the current characteristics of historic and cultural districts and easily provide effective suggestions for follow-up preservation methods.