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Evaluating soil quality is crucial for ensuring the sustainable use of agricultural lands. This review examines the definition, evaluation methods, indicator selection, and relevant case studies. The concept of soil quality supplements soil science research by deepening our understanding of soils and aiding in the allocation of resources as agriculture intensifies to meet rising global demand. Soil quality provides a framework for educating stakeholders about the essential functions of soils and offers a tool for assessing and comparing different management techniques. Regular evaluation of soil quality is vital for maintaining high crop yields and addressing the gap between production and consumption. Nowadays, many researchers have explored machine learning (ML) and deep learning (DL) techniques and various algorithms to model and predict soil quality with satisfactory results. These chosen indicators can be influenced by chemical, biological, or physical features. This paper compares ML and DL with traditional methods, examining their features, limitations, different categories of machine learning, and their applications in soil quality assessment. Finally, we show that predicting soil quality has the potential to be extremely accurate and efficient with ML and DL. This distinguishes the application of DL and ML from other approaches since they can anticipate the soil quality index without the need for more intricate computations. Our suggestion for future studies is to evaluate soil quality over broader regions and predict it by using more accurate, modern, and faster methods, using a variety of activation functions and algorithms. [ABSTRACT FROM AUTHOR]

Kidney fibrosis is the hallmark of chronic kidney disease progression; however, at present no antifibrotic therapies exist.sup.1-3. The origin, functional heterogeneity and regulation of scar-forming cells that occur during human kidney fibrosis remain poorly understood.sup.1,2,4. Here, using single-cell RNA sequencing, we profiled the transcriptomes of cells from the proximal and non-proximal tubules of healthy and fibrotic human kidneys to map the entire human kidney. This analysis enabled us to map all matrix-producing cells at high resolution, and to identify distinct subpopulations of pericytes and fibroblasts as the main cellular sources of scar-forming myofibroblasts during human kidney fibrosis. We used genetic fate-tracing, time-course single-cell RNA sequencing and ATAC-seq (assay for transposase-accessible chromatin using sequencing) experiments in mice, and spatial transcriptomics in human kidney fibrosis, to shed light on the cellular origins and differentiation of human kidney myofibroblasts and their precursors at high resolution. Finally, we used this strategy to detect potential therapeutic targets, and identified NKD2 as a myofibroblast-specific target in human kidney fibrosis. A range of techniques are used to investigate the molecular landscape of chronic kidney disease, and the results suggest that distinct populations of pericytes and fibroblasts are the main source of myofibroblasts in kidney fibrosis., Author(s): Christoph Kuppe [sup.1] [sup.2] , Mahmoud M. Ibrahim [sup.1] [sup.2] [sup.18] , Jennifer Kranz [sup.2] [sup.3] [sup.4] , Xiaoting Zhang [sup.2] , Susanne Ziegler [sup.2] , Javier Perales-Patón [sup.2] [...]

Divergent transcription, in which reverse-oriented transcripts occur upstream of eukaryotic promoters in regions devoid of annotated genes, has been suggested to be a general property of active promoters. Here we show that the human basal RNA polymerase II transcriptional machinery and core promoter are inherently unidirectional and that reverse-oriented transcripts originate from their own cognate reverse-directed core promoters. In vitro transcription analysis and mapping of nascent transcripts in HeLa cells revealed that sequences at reverse start sites are similar to those of their forward counterparts. The use of DNase I accessibility to define proximal promoter borders revealed that about half of promoters are unidirectional and that unidirectional promoters are depleted at their upstream edges of reverse core promoter sequences and their associated chromatin features. Divergent transcription is thus not an inherent property of the transcription process but rather the consequence of the presence of both forward- and reverse-directed core promoters.

Nowadays, machine learning (ML) is a useful technology due to its high accuracy in constructing non-linear models and algorithms that can adapt to the complexity and diversity of data. Thus, the current work aimed to predict the soil quality index (SQI) from extensive soil data, achieving high accuracy with the artificial neural networks (ANN) model. However, the efficiency of ANN depends on the accuracy of the data that is prepared for training. For this purpose, MATLAB programming language was used to enable the calculation, classification, and compilation of the results into databases within a few minutes. The proposed MATLAB program was highly efficient, accurate, and quick in calculating soil big data for training the machine compared with traditional methods. The database contains 306 vector sets, 80% of them are used for training and the remaining 20% are reserved for testing. The optimal model obtained comprises one hidden layer with 250 neurons and one output layer with a sigmoid function. The ANN achieved a high coefficient of determination (R2) values for SQI estimation, with around 0.97 and 0.98 for training and testing, respectively. The results indicate that 36.93% of the total soil samples belonged to the very high quality class (C1). In contrast, the high quality (C2), moderate quality (C3), low quality (C4), and very low quality (C5) classes accounted for 10.46%, 31.37%, 20.92%, and 0.33% of the samples, respectively. The high contents of CaCO3, pH, sodium saturation, salinity, and clay content were identified as limiting factors in certain areas. The results of this study indicated high accuracy of soil quality assessment using physical, chemical, and fertility soil features in regression analysis with ANN. This method, which is suitable for arid zones, enhances agricultural productivity and decision-making by identifying critical soil quality categories and constraints. [ABSTRACT FROM AUTHOR]

TUSE of nano-sulfur (NS) as a suspension to mitigate calcium carbonate (CaCO3) of calcareous sandy soil is a new strategy and has not yet been tested. In a pot experiment, this study evaluated four NS suspensions containing 15, 30, 60, and 90 kg NS ha-1, plus the control (distilled water only, 0 NS). Pots were incubated under laboratory conditions for two months. Then, total, active, and active/total CaCO3, as well as the CaCO3-related properties were estimated. The suspension containing 90 kg NS ha-1 was superior, while that containing 30 kg NS ha-1 was optimum. However, from an economic perspective, the suspension containing 15 kg NS ha-1 was preferred. With the optimum suspension, active, total, and active/total CaCO3 reduced by about 24%, 40%, and 22% compared to the control, respectively. Furthermore, the CaCO3-related properties, such as pH decreased by 0.48 unit and sodium adsorption (Na-adsorption ) ratio by 18%, while soluble Ca2+, K+, SO42-, water stable aggregate index, and macro- to micro-pores ratio increased by about 100%, 44%, 19%, 33%, and 60% compared to the control, respectively. In general, the optimal active CaCO3 for better soil properties improvement is less than 10%. The NS suspension has a high corrective power on CaCO3 and its related properties in the short term (60 days). However, regular application is recommended to continue pH reduction and neutralize CaCO3. Therefore, NS suspension may be an effective option when an immediate reaction from the amendment is needed, especially at critical stages of growth. [ABSTRACT FROM AUTHOR]

The pursuit of Sustainable Supply Chain Management (SSCM) has become increasingly vital in the face of escalating environmental, social, and economic challenges. This paper presents a novel approach that harnesses the power of m-generalized q-neutrosophic numbers (mGqNN) within the MULTIMOORAmGqNN method to evaluate and select SSCM performance and theory. By integrating mGqNN, we offer a versatile framework that adeptly navigates the uncertainties and vagueness inherent to SSCM decisionmaking. Through systematic linguistic assessments by multiple decision makers, our approach ranks SSCM alternatives, facilitating the identification of optimal strategies that enhance sustainability performance. This paper contributes to the evolving discourse on SSCM by introducing a robust methodological framework that addresses the multifaceted complexities of sustainability in supply chains. In an era where sustainability is paramount, the MULTIMOORA-mGqNN method offers researchers and practitioners a valuable tool to make informed decisions and guide supply chain strategies towards a more sustainable and responsible future. This innovative approach has the potential to reshape the landscape of SSCM, empowering organizations to forge a path towards enduring environmental stewardship, social responsibility, and economic resilience. [ABSTRACT FROM AUTHOR]

Increased Na+ ions negatively affect soil physicochemical characteristics. We ran a pot incubation experiment for 2 months and a leaching experiment for another 2 months to monitor the effects of nano-gypsum (NG, <100 nm) in low affordable doses on improving saline-sodic soil physicochemical characteristics. Treatments included a control (CK), conventional gypsum (CG; ≤ 0.5 mm) at 100% CG requirement (8.9 t ha−1: CGR), and three low doses of NG (240, 480 and 960 kg ha−1 = 2.7%, 5.4% and 10.8% of the CGR). Both applications of NG at 10.8% of CGR and the CG at 100% of CGR followed by leaching had the best effect and performed almost equally. However, the use of 960 kg NG ha−1 had the most pronounced effect, where it decreased the bulk density (16.4%) and increased stable soil structure (46.2%). Also, the soil hydraulic conductivity increased by approximately 2.4 times, which helped increase the Na-removal efficiency by 68.9%. Moreover, it decreased soil reaction, electrical conductivity and exchangeable-Na percentage by 15.9%, 39.7% and 71.4% compared to the CK, respectively. Therefore, the NG may introduce novel and alternative application strategy that allows for lower application rate with an adequate gypsum requirement. [ABSTRACT FROM AUTHOR]

THE AIM of the present study was to evaluate the effects of sandification and composted manure on some selected chemical properties; SOM, CaCO3, EC, Soluble Calcium and pH, as well as the pore size distribution of heavy clay calcareous soil. The experiment included four levels of sand addition and three levels of compost in a split plot design with three replicates. The experiment was carried out in an agricultural bilateral cycle that included wheat and maize crop cultivation in the winter and summer seasons, respectively. The results showed that sandification increased the sand fraction slightly and led to a slight decrease in the clay fraction at the end of the wheat season. This was more pronounced in the 1st soil layer than in the 2nd and the 3rd subsurface layers. The residual effects of compost (after harvesting the maize crop) had no obvious effect on the soil particle size distribution. Increasing rates of sandification and compost additions led to a decrease in the soil content of CaCO3% compared to the initial soil, pH values, and soil salinity, while the SOM% and soluble Ca ions increased. The best results were obtained under the highest rates of sandification (S15) and compost addition (C2). It could be summarized that the role of both sandification and compost additions has affected CaCO3. This relation increases the release of calcium ions (Ca+2) to soil solutions, so that buffering the pH values declines towards neutrality. The relatively high salinity of compost affected positively the calcium carbonate solubility. [ABSTRACT FROM AUTHOR]

Introduction: The purpose of this study was to develop and validate a diagnostic measure of attention deficit, hyperactivity disorder and impulsivity. Method: The instrument was administered to normal (n=51), learning disability (LD) (n=96) and intellectually disabled (ID) (n=108) children. The instrument in its final form comprises 36 items. Results: Reliability analysis, exploratory factor analysis, and confirmatory factor analysis revealed that the instrument possesses good reliability and validity estimates. MANOVA revealed that intellectually disabled, learning disabled and normal children differed significantly on all of the five subscales. The pattern of difference mainly favored I.D. children then L.D. The lowest scores on all of the subscales were those of the normal children. Discussion and Conclusion: It is important that children with L.D. be differentiated from other types of disabilities. The instrument at hand is presumed to make such distinction. Namely, the instrument is a tool to identify children with attention deficit and hyperactivity disorder. (Contains 1 figure and 3 tables.)

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