Chemical Balance Research Articles

Negative Symptoms in Schizophrenia: An Update on Research Assessment and the Current and Upcoming Treatment Landscape.

The negative symptoms of schizophrenia include diminished emotional expression, avolition, alogia, anhedonia, and asociality, and due totheir low responsiveness to available treatments,are a primary driver of functional disability in schizophrenia. This narrative review has the aim of providing a comprehensive overview of the current research developments in the treatment of negative symptoms in schizophrenia, and begins by introducing the concepts of primary, secondary, prominent, predominant, and broadly defined negative symptoms. We then compare and contrast commonly used research assessment scales for negative symptoms and review the evidence for the specific utility of widely available off-label and investigational treatments that have been studied for negative symptoms. Mechanism of action/putative treatments included are antipsychotics (D2R antagonists), N-methyl-D-aspartate receptor (NMDAR) and other glutamatergic modulators, serotonin receptor (5-HTR) modulators, anti-inflammatory agents, antidepressants, pro-dopaminergic modulators (non-D2R antagonists), acetylcholine modulators, oxytocin, and phosphodiesterase (PDE) inhibitors. With the caveat that no compounds are definitively proven as gold-standard treatments for broadly defined negative symptoms, the evidence base supports several potentially beneficial off-label and investigational medications for treating negative symptoms in schizophrenia, such as monotherapy with cariprazine, olanzapine, clozapine, and amisulpride, or adjunctive use of memantine, setrons such as ondansetron, minocycline, and antidepressants. These medications are widely available worldwide, generally tolerable and could be considered for an off-label, time-limited trial for a predesignated period of time, after which a decision to switch or stay can be made based on clinical response. Among investigational medications, NMDAR agonists, muscarinic agonists, and LB-102 remain under study. Suggestions for future research include reducing placebo effects by designing studies with a smaller number of high-quality study sites, potentially increasing the use of more precise rating scales for negative symptoms, and focused studies in people with predominant negative symptoms.

In silico modeling enables greener analytical and preparative chromatographic methods

Environmental impact of chromatographic separation at both analytical and preparative scale can be significantly reduced using In Silico Modeling.

Chemodynamic covalent adaptable network-induced robust, self-healing, and degradable fluorescent elastomers for multicolor information encryption.

Elastomers are of great significance in developing smart materials for information encryption, and their unique self-healing and highly flexible properties provide innovative solutions to enhance security and anti-counterfeiting effectiveness. However, challenges remain in the multifunctional combination of mechanical properties, self-healing, degradability, and luminescence of these materials. Herein, a chemodynamic covalent adaptable network (CCAN)-induced robust, self-healing, and degradable fluorescent elastomer is proposed. Thanks to the CCANs, the resulting elastomer exhibits a tensile strength of 33.44 MPa (300 times higher than that of a linear elastomer) and an elongation at break of 1265%, and its mechanical properties can be restored to about 20 MPa after 72 h of healing at room temperature, and a self-healing efficiency of 94.67% can be realized for 24 h at 70 °C. Simultaneously, the dynamic chemical balance of keto and enol structural transitions of curcumin chain segments can be driven by CCANs, realizing multi-color (from yellow to violet) display and broad wavelength (300-500 nm) excitation, which in turn enables surface read-write and color rosette and QR code pattern printing. In addition, it can also achieve adaptive degradation under biological, alkaline, and hot water conditions. This work has guiding significance for developing the next generation of high-performance multifunctional elastomer materials, which have potential applications in the field of smart anti-counterfeiting materials and smart flexible optoelectronics.

Integrated analysis of physiological and metabolic data uncovers essential dynamic mechanisms involved in the maturation of cigar tobacco leaves

The quality of cigar tobacco leaves is profoundly affected by the timing of their harvest, with both early and late collections resulting in inferior characteristics. While the relationship between maturity and physiological metabolic processes is acknowledged, a comprehensive understanding of the physiological behavior of cigar leaves harvested at different stages remains elusive. This research investigated the physiological and metabolomic profiles of the cigar tobacco variety CX-014, grown in Danjiangkou City, Hubei Province, with leaves sampled at 35 (T1), 42 (T2), 49 (T3), and 56 (T4) days post-inflorescence removal. Leaf color transitioned from green to yellow, accompanied by the appearance of white mature spots. Notable increases in photosynthetic pigments and gas exchange parameters occurred between T1 and T2, followed by decline at T3 and T4. The optimal sugar-to-nicotine and potassium-to-chlorine ratios, critical determinants of smoking quality and tobacco combustibility, were observed at T3, indicating a superior chemical balance in leaves harvested at this stage. Metabolomic analysis revealed 2153 distinct metabolites, with the most significant changes occurring between T2 and T3, highlighting critical physiological transformations during this interval. Pathway enrichment analysis via KEGG pinpointed notable shifts in amino acid synthesis pathways, particularly those involving tryptophan, alanine, and aspartate. Tryptophan metabolism and zeatin biosynthesis were substantially altered, with compounds like indolepyruvic acid, N-formylpurine nucleotide, isopentenyladenine nucleotide, and dihydrozeatin showing marked reductions at T3. This study also explored how the timing of lower leaf harvest influences the physiological processes of middle leaves, finding that a plethora of metabolites associated with the breakdown of arachidonic acid—a primitive metazoan signaler implicated in plant stress and defense networks—were abundant in T3 leaves when lower leaves were harvested 43–38 days prior. Overall, these findings elucidates the complex physiological dynamics of cigar leaves during maturation, highlighting critical metabolites involved in essential metabolic pathways.

Surface Modifications and Antifungal Efficacy of Origanum Oil Incorporation in Denture-based Materials: An In Vitro Study.

This study aimed to assess if the addition of origanum oil to denture materials could decrease microorganisms counts and biofilm formation without changing their mechanical/surface properties. A total of 66 resilient denture liner discs (SoftConfort, Dencril Comércio de Plásticos Ltda, SP, Brazil) were prepared with fixed dimensions of 10 × 3 mm for biofilm assay (n = 36) and 12 × 2 mm for sorption-solubility tests (n = 30) containing three oil concentrations - 0, 2.5 and 5%, thereby n = 12 per each group samples for biofilm assay and n = 10 per each group for sorption-solubility test respectively. While the microcosm biofilms of Streptococcus mutans, Candida species, and total microorganisms formed on denture liner were counted and expressed as colony-forming units per disc surface area, the water sorption (WS) and solubility (SL), was calculated by weighing the samples with an analytical balance at different intervals after storing them in a desiccator and distilled water alternatively. Data was recorded and statistically analyzed. Surface roughness increased in all groups after biofilm formation (p < 0.001), with the most significant change observed in the 2.5% oil group. The tested oil concentrations did not result in sorption changes, but a 5% oil concentration resulted in higher solubility (p < 0.001). A reduction of total microorganisms and S. mutans was seen after 24 hours for all concentrations (p < 0.05). No significant reduction was found for Candida (C.) albicans after 24 hours, whilst 2.5% oil concentration presented lower counts of C. albicans in comparison to the 5% group after 24 hours (p < 0.05). Incorporating 2.5% origanum essential oil into resilient denture liners seems to reduce microorganisms count in a complex biofilm model. These results need to be confirmed in future studies. The addition of natural products like origanum oil into denture-based materials can help manage biofilm onset and development while offering a simple and effective approach for maintaining denture hygiene. This strategy enhances the antimicrobial properties of denture liners without significantly altering their mechanical and surface characteristics, potentially improving patient outcomes. How to cite this article: Feitosa MÁL, Poletto-Neto V, Maske TT, et al. Surface Modifications and Antifungal Efficacy of Origanum Oil Incorporation in Denture-based Materials: An In Vitro Study. J Contemp Dent Pract 2024;25(9):878-884.

Tissue Sources Influence the Morphological and Morphometric Characteristics of Collagen Membranes for Guided Bone Regeneration.

(1) Background: Collagen, a natural polymer, is widely used in the fabrication of membranes for guided bone regeneration (GBR). These membranes are sourced from various tissues, such as skin, pericardium, peritoneum, and tendons, which exhibit differences in regenerative outcomes. Therefore, this study aimed to evaluate the morphological and chemical properties of porcine collagen membranes from five different tissue sources: skin, pericardium, dermis, tendons, and peritoneum. (2) Methods: The membrane structure was analyzed using energy-dispersive X-ray spectrometry (EDX), variable pressure scanning electron microscopy (VP-SEM), Fourier transform infrared spectroscopy (FTIR), and thermal stability via thermogravimetric analysis (TGA). The absorption capacity of the membranes for GBR was also assessed using an analytical digital balance. (3) Results: The membranes displayed distinct microstructural features. Skin- and tendon-derived membranes had rough surfaces with nanopores (1.44 ± 1.24 µm and 0.46 ± 0.1 µm, respectively), while pericardium- and dermis-derived membranes exhibited rough surfaces with macropores (78.90 ± 75.89 µm and 64.89 ± 13.15 µm, respectively). The peritoneum-derived membrane featured a rough surface of compact longitudinal fibers with irregular macropores (9.02 ± 3.70 µm). The thickness varied significantly among the membranes, showing differences in absorption capacity. The pericardium membrane exhibited the highest absorption, increasing by more than 10 times its initial mass. In contrast, the skin-derived membrane demonstrated the lowest absorption, increasing by less than 4 times its initial mass. Chemical analysis revealed that all membranes were primarily composed of carbon, nitrogen, and oxygen. Thermogravimetric and differential scanning calorimetry analyses showed no significant compositional differences among the membranes. FTIR spectra confirmed the presence of collagen, with characteristic peaks corresponding to Amide A, B, I, II, and III. (4) Conclusions: The tissue origin of collagen membranes significantly influences their morphological characteristics, which may, in turn, affect their osteogenic properties. These findings provide valuable insights into the selection of collagen membranes for GBR applications.

Methanesulfonic acid in chiral supercritical fluid chromatography: the quest for perfect peaks and the trials of resolution

The increase in complexity of chiral molecules produced in the pharmaceutical industry often results in difficulties when separating stereoisomers in both analytical and preparative scale. Screening chiral selectors, modifiers, and additives is time consuming; however, it is the current go-to solution to solve challenging cases. In this study, methanesulfonic acid (MSA) was evaluated as an additive for the supercritical fluid chromatographic separation of stereoisomers from a series of analogue compounds, using four types of chiral selectors; the results were compared to those obtained from more common additives such as formic acid, trifluoroacetic acid, and diethylamine. All eight compounds were baseline resolved on at least two of the four columns tested when using MSA, and overall MSA gave the highest number of resolved peaks and the most symmetrical peaks, when compared to the use of the other additives tested. As proof of principle, one of the compounds was successfully used for testing the scale up from analytical to preparative separation. Based on the results, MSA showed promise as an additive for the separation of polar species in chiral SFC, without the need for water in the modifier. Though not a universal solution to resolving stereoisomers, MSA could help in cases where resolution is lost due to peak tailing or high retention.

E. coli transcription factors regulate promoter activity by a universal, homeostatic mechanism.

Transcription factors (TFs) may activate or repress gene expression through an interplay of different mechanisms, including RNA polymerase (RNAP) recruitment, exclusion, and initiation. TFs often have drastically different regulatory behaviors depending on promoter context and interacting cofactors. However, the detailed mechanisms by which each TF affects transcription and produce promoter-dependent regulation is unclear. Here, we discover that a simple model explains the regulatory effects of E. coli TFs in a range of contexts. Specifically, we measure the relationship between basal promoter activity and its regulation by diverse TFs and find that the contextual changes in TF function are determined entirely by the basal strength of the regulated promoter: TFs exert lower fold-change on stronger promoters under a precise inverse scaling. Remarkably, this scaling relationship holds for both activators and repressors, indicating a universal mechanism of gene regulation. Our data, which spans between 100-fold activation to 1000-fold repression, is consistent with a model of regulation driven by stabilization of RNAP at the promoter for every TF. Crucially, this indicates that TFs naturally act to maintain homeostatic expression levels across genetic or environmental perturbations, ensuring robust expression of regulated genes .

The Role of Sudanese Forests Agro-ecosystem in Improving the Cation Exchange Capacity and Exchangeable Bases of a Sandy Soil in Western Burkina Faso

The objective of this study is to evaluate the influence of Sudanese forests on the chemical equilibrium and exchangeable bases of sandy soils in the Bondoukuy region. The study examines the impact of forest cover on soil fertility and the mechanisms through which these natural ecosystems promote soil health and productivity in the region. The Cation Exchange Capacity (CEC) of soil represents its capacity to retain and exchange nutrient cations with plant roots. In Sudanian Forests Agro-ecosystem, sandy soils are characterized by a dominance of coarse particles, which exhibit a low specific surface area and low cation retention capacity, in contrast to clayey soils. A low CEC restricts soil fertility and the capacity of the soil to sustainably provide essential nutrients to plants. It was hypothesized that the CEC of the topsoil in forests of western Burkina Faso would demonstrate a significant increase due to substantial environmental changes during the fallow period. To test this hypothesis, a comparison was made between soil fertility in forests and fallows, and that of cultivated plots, which were considered the control for this increase. A total of 15 plots were selected, with five plots allocated to each situation. The vegetation and soil of the plots were described in detail. Composite soil samples were taken from the 0-20 cm horizon. The soil analyses in the laboratory were conducted on several parameters, including texture, pH-H2O, pH-KCl, carbon, nitrogen, CEC and exchangeable bases. The results of the observations enabled the classification of the soils as tropical ferruginous soils with iron and manganese sesquioxides. The original materials indicate that the soil exhibits sandy properties. This was demonstrated by the granulometric study, which revealed that the soils under investigation exhibited an essentially sandy texture on the surface horizon. This results in a low retention capacity for exchangeable bases. The woody vegetation of the forest exhibits a greater diversity of flora than that of the fallows. This has a significant impact on the enhancement of the CEC, due to the replenishment of the soil with plant debris of varying organic compositions. The overall pH is slightly acidic, with a range of 5.63 to 5.71. The soils of forests exhibit higher concentrations of carbon and nitrogen than those of the fallows and fields. The observation of chemical balances has enabled the identification of forests as an environment conducive to optimal plant nutrition. Overall, forests enhance the CEC and exchangeable bases, despite the values being below the threshold recommended for tropical sandy soils. It is therefore necessary to implement measures to promote sustainable agriculture and enhance agricultural productivity in this region, where soil nutrients are naturally scarce.

Automatic kidney disease prediction using deep learning techniques

The kidneys play an energetic role in eliminating excess products and fluids from the body, by a complex mechanism which is crucial for upholding a stable balance of body chemicals. Chronic kidney disease (CKD) is considered by an unhurried weakening in renal function that may eventually result in kidney injury or failure. The difficulty of diagnosing the illness rises as it worsens. However, using data from normal medical visits to evaluate the various phases of CKD could help with early detection and prompt care. Researchers suggest a classification strategy for CKD along with optimization strategies used in the learning process. The incorporation of artificial intelligence offers promise because it may often astonish with its skills and enable seemingly difficult undertakings. Modern machine learning techniques have been developed to detect renal illness in light of this. In the current study, a new deep learning model for CKD initial recognition and prediction is introduced. The main objective of the project is to build a strong deep neural network (DNN) and estimate its result outcomes in comparison to other leading-edge machine learning techniques. The outcomes demonstrate that the proposed strategy outperforms current approaches and has promise as a useful tool for CKD detection.

In pursuit of precise Ca II H&amp;K chromospheric surface fluxes

The emission lines of the Ca II H&amp;K doublet present one of the most important channels of radiative cooling for the chromospheres of cool stars. Although most other line emissions (Mg II h&amp;k and numerous iron lines) populate the far-UV and require a very competitive space-born observing time, the Ca II H&amp;K lines in the optical UV are easily accessible by ground-based spectroscopy with relatively cheap instrumentation, and the observational data are plentiful. At the same time, in order to include realistic mechanical and magnetic heating, advanced chromospheric models now require radiative cooling losses in absolute terms and therefore call for a precise surface flux scale, which could be provided by matching a photospheric Ca II H&amp;K line profile computed by photospheric models. However, a major obstacle here is the significant ambiguity in parameter space in the face of a very sensitive dependence of cool stellar optical UV surface fluxes on the effective temperature. Consequently, we have developed a rigorous method by which precise physical parameters, most notably the effective temperature, are first determined by ISPEC tools working on high signal-to-noise spectra and based on a suitable line list and reference continuum. However, crosstalk and navigation of multiple local chi-square minima (best solutions) in parameter space must be considered. Only with the optimal set of parameters is a single PHOENIX model calculated, which defines the spectral surface flux scale in the Ca II H&amp;K line region. In this paper, we discuss the results of the absolute measurements of Ca II K line fluxes for 32 representative cool stars and the accuracy of this approach. Finally, we show a comparison of the Ca II K chromospheric fluxes with line widths, and a relation of chromospheric flux with effective temperature and gravity following the logarithmic form log FCa II = α′ log g + β′ log Teff + C′.

Utilization of Waste Glass in Cement Clinker Production: Impacts on Quality, Process Efficiency and Environmental Benefits

The incorporation of waste glass as a component in clinker production presents a sustainable approach to addressing critical challenges in the cement industry, including the reduction of CO2 emissions and effective waste management. Waste glass, characterized by its high silica content and alkali properties, can serve as an alternative alkali source in clinker manufacturing, replacing traditional raw materials and regulating the alkali-sulphur ratio. This dual functionality not only optimizes the chemical balance in the kiln process but also enhances clinker quality by controlling phase formation. The utilization of waste materials in industrial processes is increasingly significant in promoting circular economy principles. Integrating waste glass reduces the dependence on natural raw materials such as limestone and clay, which are associated with high energy and CO2 emission intensities during production. Furthermore, waste glass contributes to a reduction in the carbon footprint of cement production by facilitating lower-temperature clinkering, thus cutting energy consumption and greenhouse gas emissions. This study highlights the potential of waste glass as a viable alternative in clinker production, emphasizing its importance in achieving sustainability goals. Beyond the environmental benefits, adopting waste materials in industrial applications contributes to waste diversion from landfills, resource conservation, and cost efficiencies, aligning with global efforts to mitigate climate change and promote sustainable development.

Diversification of 3′‐Amino Nucleosides via Enzymatic Transglycosylation

AbstractA systematic study on the enzymatic transglycosylation of 3′‐amino nucleosides has been reported. Using the commercially available 3′‐amino‐3′‐deoxythymidine as the sugar donor, 13 pyrimidine and purine nucleobases were shown as the viable sugar acceptors in the nucleoside phosphorylase EcPPNP‐ and/or nucleoside 2′‐deoxyribosyltransferase LlNDT‐catalyzed reactions, furnishing the corresponding 3′‐amino containing nucleosides in 23%–73% NMR yields at an analytical scale. The synthetic utility of this diversification approach was further showcased by the successful synthesis of 3′‐amino‐2′,3′‐dideoxy‐5‐bromouridine in 29% isolated yield at a preparative scale. The current work demonstrates the transglycosylation activity of EcPPNP for the first time, expands the substrate scope of LlNDT, and provides an efficient, direct access to various 3′‐amino nucleosides of high value.

Thermodynamic properties of pinned nanobubbles.

We present molecular dynamics simulations to study the thermodynamics of nanobubbles trapped at the mouth of narrow slit pores. Except when the slit dimensions are comparable to typical molecular sizes, the predictions of macroscopic thermodynamic theory are recovered by our simulations. Our simulations confirm that in this case, the internal pressure of stable nanobubbles is independent of the bubble radius and the surface tension and only depends on the bulk properties of the solute-containing solution, i.e., the chemical potential balance. However, in the case of extreme confinement, the pressure is not a suitable quantity to describe the thermodynamics of the bubbles, while the balance of the chemical potentials is.

Identifying Ordinal Similarities at Different Temporal Scales

This study implements the permutation Jensen–Shannon distance as a metric for discerning ordinal patterns and similarities across multiple temporal scales in time series data. Initially, we present a numerically controlled analysis to validate the multiscale capabilities of this method. Subsequently, we apply our methodology to a complex photonic system, showcasing its practical utility in a real-world scenario. Our findings suggest that this approach is a powerful tool for identifying the precise temporal scales at which two distinct time series exhibit ordinal similarity. Given its robustness, we anticipate that this method could be widely applicable across various scientific disciplines, offering a new lens through which to analyze time series data.

Reversible Solid Oxide Cells: A Study on System Simulation

Introduction In recent years, renewable energy such as solar and wind power has been attracting attention as clean energy. However, such power generation exhibits significant fluctuations in output depending on weather conditions and time of day. During times when renewable power generation exceeds electricity demand, it would be beneficial to utilize such excess renewable electricity to produce hydrogen, to be stored it in storage tanks. In contract, if the supply of electricity is insufficient to meet the electricity demand, the fuel cell operation can provide electricity.Reversible solid oxide cells (r-SOC) can be a core device for reversible energy systems because they can reversibly switch between fuel cell operation and steam electrolysis using the same cell. When incorporating r-SOC into a system, the heat management and storage methods have to considered in the system design1 2. However, it is not easy to evaluate the performance of prototype systems with various combinations of BOPs (balance-of-plants), such as evaporators and heat exchangers. The objective of this study is therefore to propose a concept of such a reversible energy system which enables power generation, energy storage, and their control using reversible cells, through system simulation. Experimental In this study, we used chemical engineering simulation software (Aspen Plus) to calculate the chemical reaction processes and heat balance based on thermodynamic and chemical equilibria. Figure 1 (a) shows a simplified diagram of a r-SOC system using a reversible solid oxide cell. This r-SOC system was assumed to use a common storage tank for both SOEC and SOFC modes, in which H2 and H2O were stored. It was also assumed that the direction of fuel flow is the same in both modes and that the two modes can be switched by switching the direction of the current only. This system was built on Aspen Plus and simulated. In this study, we calculated the efficiency of a 1kW-class SOEC system for hydrogen production, assuming the hydrogen produced in the SOEC mode is consumed in the SOFC mode within a continuous operation period of 24 h (a fixed period of time).Simulations were also performed assuming different storage methods. Hydrogen has a low volumetric energy density, so its density must be increased when it is stored. Here, hydrogen storage alloys and high-pressure hydrogen tanks were considered as typical examples.As a typical property of hydrogen storage alloys, the reaction is exothermic during hydrogen absorption and endothermic during hydrogen desorption3 4. We constructed a system that utilizes this feature.Simulation models for the high-pressure hydrogen tanks were created assuming 35MPa and 70MPa tanks. Compression was assumed to be isentropic. Results and Discussion Figure 1 (b) shows that the efficiency in the SOFC mode with different current densities and corresponding operation times, assuming that hydrogen produced in the SOEC mode is fully consumed in the SOFC mode as described above. The black symbols/line shows the efficiency when using a hydrogen storage alloy, and the blue and red symbols/line show the efficiency when using a 35 MPa and 70 MPa high-pressure tanks, respectively. When hydrogen storage alloys were used, the efficiency was almost equal to that of reversible systems. It was also found that compactness can be achieved by the storage method without pressurization. When using a high-pressure hydrogen tank, the entire system could be downsized, but the efficiency decreased due to energy loss in pressurization. The use of hydrogen storage alloys is expected to realize a reversible energy system capable of highly-efficient power generation, hydrogen production, and reversible operation.

Enhanced Cation Exchange Capacity and Exchangeable Bases in Sandy Soil Across Sudanian Savanna Agro-Ecosystem of Bondoukuy, Western Burkina Faso

The Cation Exchange Capacity (CEC) of soil represents its capacity to retain and exchange nutrient cations with plant roots. In Sudanian savannas, sandy soils are characterized by a dominance of coarse particles, which exhibit a low specific surface area and low cation retention capacity, in contrast to clayey soils. A low CEC restricts soil fertility and the capacity of the soil to sustainably provide essential nutrients to plants. It was hypothesized that the CEC of the topsoil in savannas of western Burkina Faso would demonstrate a significant increase due to substantial environmental changes during the fallow period. To test this hypothesis, a comparison was made between soil fertility in savannas and fallows, and that of cultivated plots, which were considered the control for this increase. A total of 15 plots were selected, with five plots allocated to each situation. The vegetation and soil of the plots were described in detail. Composite soil samples were taken from the 0-20 cm horizon. The soil analyses in the laboratory were conducted on several parameters, including texture, pH-H2O, pH-KCl, carbon, nitrogen, CEC and exchangeable bases. The results of the observations enabled the classification of the soils as tropical ferruginous soils with iron and manganese sesquioxides. The original materials indicate that the soil exhibits sandy properties. This was demonstrated by the granulometric study, which revealed that the soils under investigation exhibited an essentially sandy texture on the surface horizon. This results in a low retention capacity for exchangeable bases. The woody vegetation of the savannas exhibits a greater diversity of flora than that of the fallows. This has a significant impact on the enhancement of the CEC, due to the replenishment of the soil with plant debris of varying organic compositions. The overall pH is slightly acidic, with a range of 5.63 to 5.71. The soils of the savannas exhibit higher concentrations of carbon and nitrogen than those of the fallows and fields. The observation of chemical balances has enabled the identification of savannas as an environment conducive to optimal plant nutrition. Overall, the savannas enhance the CEC and exchangeable bases, despite the values being below the threshold recommended for tropical sandy soils. It is therefore necessary to implement measures to promote sustainable agriculture and enhance agricultural productivity in this region, where soil nutrients are naturally scarce.

Optimizing Machine Learning Models with Data-level Approximate Computing: The Role of Diverse Sampling, Precision Scaling, Quantization and Feature Selection Strategies

Efficiency, low-power consumption, and real-time processing in embedded machine learning implementations are critical, particularly for models deployed in environments with large-scale data processing and resource-constrained environments. This paper investigates the application of approximate computing techniques as a viable solution to reduce computational complexity and optimize machine learning models, focusing on two widely used supervised machine learning models: k-nearest neighbors (KNN) and support vector machines (SVM). Although many studies compare machine learning classification techniques, the combined use of optimization strategies remains underexplored. Specifically, the combined utilization of feature selection, sampling, quantization, precision scaling, and relaxation methods for the purpose of optimizing and acquiring training and validation data is underexplored, particularly within the context of medical diagnosis datasets. In this paper, we propose a framework that uses data-level approximate computing techniques, including by diverse sampling strategies, precision scaling, quantization, and feature selection methods, to evaluate the impact of these techniques on the computational efficiency and accuracy of KNN and SVM models. Experimental results demonstrate that with careful application of approximate computing strategies, especially in critical applications such as medical diagnosis, it is possible to achieve considerable gains in efficiency while maintaining acceptable levels of accuracy. The combined application of these methods by selecting 3 features and quantizing the data values to 8 levels, then applying random sampling with 30% reductions and scaling the precision at 5 bits resulted in reductions of 87.5% in computation, 76.9% in memory usage, and 17% in delay, without any degradation in accuracy, as validated by tenfold cross-validation, Training Data validation, and full dataset validation. This study confirms the potential of approximate computing to optimize machine learning workflows, making it particularly suitable for applications with limited computational resources. The source code is publicly available online https://github.com/AyadMDalloo/DatalvlAxC.

A Simplified Two-Fluid Model Based on Equilibrium Closure for a Dilute Dispersion of Small Particles

Two-fluid formalisms that fully account for all complex inter-phase interactions have been developed based on a rigorous ensemble-averaging procedure. Here, we apply equilibrium approximation to particle velocity to simplify two-phase flow equations for the case of a dilute dispersion of particles much smaller than the flow scales. First, we extend an earlier approach to consider the rotational motion of the particles and seek an equilibrium approximation for the angular velocity of the particulate phase. The resulting explicit knowledge of the particulate phase translational and rotational velocities in terms of fluid velocity eliminates the need to consider the momentum equations for the particulate phase. The equilibrium approximations also provide precise scaling for various terms in the governing equations of the two-fluid model, based on which a simplified set of equations is obtained here. Three different regimes based on the relative strength of gravitational settling are identified, and the actual form of the simplified two-phase flow equations depends on the regime. We present two simple examples illustrating the use of the simplified two-fluid formalism.

Chromatographic properties of deamidated peptides with Asn-Gly sequences in proteomic bottom-up experiments

Studies surrounding deamidation have relied on the chromatographic and mass spectrometric differentiation of Asn containing peptides and their isomeric Asp and isoAsp products. The development of mass spectrometry analytical techniques and characterization of isomer specific fragmentation patterns has permitted the investigation of some deamidation species but has struggled to remain effective when applied and on complex samples or in high throughput scenarios. On the other hand, chromatographic separations can provide additional information to facilitate detection of deamidation. In this work the retention characteristics of deamidation products have been reported in reversed-phase separations using formic acid as an ion-pairing modifier. We found three major elution patterns depending on primary and secondary structure of Asn-Gly containing tryptic peptides. Random coil, helical conformations, and N-terminal positioning of Asn usually result in Asn < isoAsp < Asp, isoAsp < Asn < Asp, and Asn < Asp < isoAsp elution order, respectively. These trends, found from the analyses of proteomic samples, were subsequently confirmed via analytical scale UV-HPLC. Additionally, we determined the retention shifts following deamidation for twenty various separation settings used as a first-dimension fractionation for high-throughput proteomic 2D LC-MS/MS analyses.