Variation Of Concentration Research Articles

Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy.

Quantification of metabolites concentrations in institutional unit (IU) is important for inter-subject and long-term comparisons in the applications of magnetic resonance spectroscopy (MRS). Recently, deep learning (DL) algorithms have found a variety of applications on the process of MRS data. A quantification strategy compatible to DL base MRS spectral processing method is, therefore, useful. This study aims to investigate whether metabolite concentrations quantified using a convolutional neural network (CNN) based method, coupled with a scaling procedure that normalizes spectral signals for CNN input and linear regression, can effectively reflect variations in metabolite concentrations in IU across different brain regions with varying signal-to-noise ratios (SNR) and linewidths (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels associated with metabolite predictions. In vivo MRS spectra were acquired from three brain regions of 43 subjects using a 3T system. The metabolite concentrations in IU of five major metabolites, quantified using CNN and LCModel, exhibit similar ranges with Pearson's correlation coefficients ranging from 0.24 to 0.78. The SE of the metabolites shows a positive correlation with Cramer-Rao lower bound (CRLB) (r=0.46) and absolute CRLB (r=0.81), calculated by multiplying CRLBs with the quantified metabolite content. In conclusion, the CNN based method with the proposed scaling procedures can be employed to quantify in vivo MRS spectra and derive metabolites concentrations in IU. The SE can be used as error index, indicating predicted uncertainties for metabolites and sharing information similar to the absolute CRLB.

Green infant formula analysis: Optimizing headspace solid-phase microextraction of carbonyl compounds associated with lipid peroxidation using GC-MS and pentafluorophenylhydrazine derivatization

The refinement and optimization of a method combining headspace solid-phase microextraction (HS-SPME) with gas chromatography-mass spectrometry (GC-MS) was successfully performed for the first time to determine seven carbonyl and dicarbonyl compounds, including glyoxal, methylglyoxal, dimethylglyoxal, and malondialdehyde in infant formulae, related to lipid peroxidation. HS-SPME was utilized for simultaneous extraction and derivatization with pentafluorophenylhydrazine (PFPH). Critical parameters such as temperature, pH, extractive phase, and salting-out were meticulously investigated and fine-tuned by an asymmetrical 2232//9 screening design to ensure the method's efficacy and reliability. Optimal conditions included a PFPH concentration of 5 g/L, pH 5.0, head-space extraction at 60 °C within 10 min, utilizing a DVB/CAR/PDMS coating, and a 20% w/w salting-out. The analytical validation of this method, compliant with FDA guidelines, demonstrated exceptional linearity, sensitivity, specificity, precision (RSD ≤13.8%), and accuracy (84.8% ≤ recovery ≤111.5%). The metric approach AGREEprep confirms its eco-friendliness, marking a significant step towards an environmentally conscious approach in infant formula analysis. An occurrence study conducted on 25 infant formula samples revealed widespread carbonyl and dicarbonyl compounds in both powdered and liquid variants. ANOVA results exhibited variations in compound concentrations among different sample groups. Clustering analyses delineated distinct groups based on carbonyl content, indicating the potential of these compounds as markers for lipid peroxidation and food quality assessment. This method serves as a valuable tool for evaluating infant formula quality, stability towards oxidation, and safety.

Enhanced Mechanistic Understanding Through the Detection of Radical Intermediates in Organic Reactions.

Two applications of a radical trap based on a homolytic substitution reaction (SH2') are presented for the trapping of short-lived radical intermediates in organic reactions. The first example is a photochemical cyanomethylation catalyzed by a Ru complex. Two intermediate radicals in the radical chain propagation have been trapped and detected using mass spectrometry (MS), along with the starting materials, products and catalyst degradation fragments. Although qualitative, these results helped to elucidate the reaction mechanism. In the second example, the trapping method was applied to study the radical initiation catalyzed by a triethylboronoxygen mixture. In this case, the concentration of trapped radicals was sufficiently high to enable their detection by nuclear magnetic resonance (NMR). Quantitative measurements made it possible to characterize the radical flux in the system under different reaction conditions (including variations of solvent, temperature and concentration) where modelling was complicated by chain reactions and heterogeneous mass transfer.

Numerical investigation and dynamics of pollutant dispersion in underground restroom ventilation

Urbanization is expanding in both surface and underground space, where the air quality in underground space restrooms in public transportation hubs is a huge challenge due to variations in passenger’ flow and specific features. The pollution in the underground restrooms mainly comes from the microorganisms and odors of human waste. This paper proposes a back-side air exhaust method for the restroom compartment to enhance pollutants removal efficiency, and an optimal design study is performed to determine the best device position, evacuation pressure, and air exchange rate. In response to research gaps, we introduced the influence of unsteady human breathing behavior and human obstacles coupled with the flow field and the pollutants diffusion in the CFD model. After model verification, we numerically investigated the effect of different ventilation modes. The results show that the top evacuation mode may accelerate the diffusion of pollutants. In contrast, the pollutant concentration around the mouth and the spillage outward from the door cracks are significantly reduced under the new ventilation mode. The standardized and actual concentrations of the pollutant and the normalized exposure index were used to assess the indoor air quality. Finally, we put forward a series of dynamical models to describe the variation of indoor pollutant concentrations over time. The study's results will provide reliable theoretical and numerical support to improve the air quality of public restrooms in urban underground spaces.

Evaluation of neutronic characteristics of accident tolerant fuel concepts in SMART reactor fuel assemblies using DRAGON

The selection of perfect accident-tolerant nuclear reactor fuels necessitates a neutronic study of the various fuels for Small Modular Reactors (SMRs). This study demonstrates a neutronic assessment of 17 × 17 fuel assemblies of Korean SMART (System-Integrated Modular Advanced ReacTor) integral type SMR using three types of Accident Tolerant Fuel (ATF) by the deterministic code DRAGON. These reactors are less expensive to build, have a shorter construction period, are safer, and have a higher power density. Uranium Nitride (UN), Uranium Di-Silicide (U3Si2), and Composite material (70 % UN- 30 % U3Si2) are considered the most potential ATF candidates. The effective multiplication factor (keff), different fissionable materials, neutron poisons, neutron-induced power, decay power, and fuel burnup are investigated as well as presented on graphs and compared from the conventional fuel UO2. Throughout the entire duration of the operation, all four models maintain a critical state while maintaining consistent dimensions. However, due to enrichment and chemical composition differences, it is found that the UN has the highest keff value. UN displayed lower concentration variation for different isotopes, whereas UO2 showed the highest power analysis study among the models. The different approaches to Accident Tolerant Fuel are not quite significant. However, opting for Uranium Nitride (UN) could offer a slight advantage in terms of effectiveness because of the higher power-up rates and burnup as well as increased cycle length when compared to the other three options.

Fluorescence-Based Aqueous Phosphate Sensing Using Eu(cpboda)(DMF)2

Fluorescence-based phosphate sensing using phosphate-sensitive phosphors is a promising approach for in situ monitoring of phosphate pollution in waterways and reservoirs. To date, the most sensitive phosphor developed for this purpose is Tb(cpboda)(DMF)2, where cpboda = (3,3′-((5-Carboxy-1,3-phenylene)bis(oxy))dibenzoic acid). In this study, we further improve this sensitivity by replacing the Tb3+ ions with Eu3+ ions to make Eu(cpboda)(DMF)2 and find concentration-independent phosphate-sensitivity of 1570 ± 120, which is ≈8× more sensitive than the Tb-version. This improvement is attributed to Eu3+ having a hypersensitive transition, while Tb3+ does not. Additionally, we characterize the phosphor’s optical properties, photodegradation, and water solubility. We find that the phosphor presents challenges with regards to both photodegradation and solubility, as it is found to be poorly soluble in water and is quickly photodegraded under UV radiation <360 nm. However, these obstacles can, in theory, be overcome with the use of direct excitation of the Eu3+ ions at 394 nm and careful design of an analysis instrument to reduce concentration variations.

Assessment of seasonal variations in vehicular emissions at traffic intersections in Ibadan using AERMOD dispersion model

AbstractAfrican cities grapple with urban air pollution from traffic‐related air pollutants (TRAPs). This study investigated TRAPs concentration variations at traffic intersections (TIs) in Ibadan, Nigeria. AERMOD model was employed to examine TRAPs dispersion at 25 selected TIs, considered as volume sources. Seasonal concentration distributions of six pollutants (CO, NO2, SO2, TVOCs, PM2.5, and PM10) were determined using meteorological, topographical, and pollutants’ emission rates in AERMOD. Estimated peak concentrations of the studied pollutants were generally higher in the rainy season than dry season, surpassing the air quality standards set by World Health Organization (WHO) and Nigeria's National Environmental Standards and Regulations Enforcement Agency (NESREA) during the rainy and dry seasons except SO2 (24 h) concentration which did not exceed the NESREA standard. This study highlighted TIs as significant contributors to air quality degradation in both seasons studied in Ibadan and showed AERMOD's suitability for air dispersion studies.

Evaluating the multi-variable influence on O3, NO2, and HCHO using BRTs and RF model

This study addresses significant knowledge gaps in understanding the complex interplay between atmospheric chemistry and synoptic conditions. Using emerging machine learning techniques—Boosted Regression Trees (BRTs) and Random Forest (RF) models—we investigate the influence of synoptic conditions on pollutant levels. Several BRTs and RF models are developed to estimate surface concentrations of ozone (O3), nitrogen dioxide (NO2), and formaldehyde (HCHO). By considering a range of algorithmic structures and explanatory variables for each pollutant, the research aims to identify the most skillful predictive approaches and influential factors governing pollutant levels. The design seeks to highlight key determinants of concentration patterns without constraining the investigation to pre-defined model structures or explanatory variable sets. Introducing a novel methodology, Correlation Coefficient Differential Evaluation (C2DE), we quantitatively assess the influence of explanatory variables. C2DE reveals significant contributions from spatial variables (i.e., trajectory clusters at varying altitudes), formaldehyde to nitrogen dioxide ratio (FNR), and meteorological parameters. Specifically, spatial variables contribute approximately 28 % to O3 concentrations, while the FNR accounts for around 5.2–9.8 % of the overall influence. For NO2 and HCHO, spatial variables contribute around 26.5 % and 32.1 %, respectively. Moreover, when considering the combined influence of meteorological parameters, these collectively explain about 45.34 %, 35.31 %, and 45.41 % of the variations in O3, NO2, and HCHO concentrations, respectively. Thus, C2DE provides valuable insights into the relative contributions of these factors, aiding in the comprehensive evaluation of air quality dynamics. This underscores the need for a multifaceted approach to comprehending and effectively addressing air pollution before devising its control strategies.

Unravelling the impact of soil types on zinc, iron, and selenium concentrations in grains and straw of wheat/Amblyopyrum muticum and wheat/Triticum urartu doubled haploid lines

The concentration of mineral nutrients in plants is associated with bioavailabilities of soil mineral nutrients, which are regulated by various soil physio-chemical properties. A pot experiment was conducted to investigate the effects of soil type on grain and straw zinc (Zn), iron (Fe) and selenium (Se) concentrations of wheat/Amblyopyrum muticum and wheat/Triticum urartu doubled haploid lines. A set of 42 treatments in a factorial combination with 21 genotypes and two soil types collected from Ngabu and Chitedze Research Stations in Malawi was laid in a randomised complete block design (RCBD) in three replicates. Pre-experiment soil Zn and Fe were extracted using DTPA extraction method followed by analysis with inductively coupled plasma-mass spectrometry (ICP-MS). Aqua-regia hotplate acid digestion was used to extract soil Se and analysis was done using ICPM-MS. Grain and straw samples were digested using nitric acid digestion (HNO3) and analysed using ICP-MS. Soil analysis results showed that the two soils had the same textural class (Sandy clay loam), but different mineral concentrations, pH levels and percentage organic matter. Analysis of variance revealed a ~two-fold higher Zn concentration in grains grown in low pH, high Zn soils (Chitedze soils) compared to grains grown in high pH, low Zn soils (Ngabu soils). Variation in grain Zn concentration was associated with the genotypes (p = 0002), soil type (p = <0.0001), and their interaction (p = 0.035). Grain Fe was 1.3-fold higher in low pH than in high pH soils, and it was influenced by genotypes (p = < 0.0001) and soil type (p = <0.0001). Grain Se was highly associated with soil type (p = <0.0001), and it was 30-fold higher in high pH than in low pH soils. Straw Zn was generally higher in plants grown in Chitedze soils than Ngabu soils, whilst straw Se was higher in plants grown in Ngabu soils than Chitedze soils. The findings demonstrate the significance of soil physio-chemical properties for mineral accumulation and distribution to plant parts, thus informing future breeding programs on important considerations on crop genetic biofortification with the three mineral elements.

Optimizing Sb2Se3 thin-film solar cells: A comprehensive simulation study of multiple influential factors

This article presents a comprehensive simulation study of Sb2Se3-based thin-film solar cells, exploring critical parameters that influence their performance and efficiency. We demonstrate that tuning the Sb2Se3 thickness offers a versatile approach to optimize light absorption and charge transport, offering promising avenues for efficiency enhancement. Furthermore, we highlight the pivotal role of carrier concentration variation in balancing carrier mobility and recombination, enabling fine-tuned control over device performance. Investigating the work function values of back contact metals uncovers crucial insights into open-circuit voltage, short-circuit current, and efficiency. Temperature effects are examined, emphasizing the need for effective thermal management strategies to ensure stable and efficient operation over a wide range of environmental conditions. The study underscores the significance of the HTL in improving charge carrier collection and reducing recombination losses. Lastly, our exploration of cadmium-free design underscores the industry's commitment to sustainable and environmentally friendly photovoltaic technologies. Overall, this simulation study advances our understanding of Sb2Se3-based solar cells and offers valuable guidance for optimizing their performance, ultimately contributing to the development of efficient and eco-friendly renewable energy solutions. Post-optimization, our findings indicate an impressive efficiency of 23.39%, with a noteworthy efficiency level of 23.02% achieved for cadmium-free solar cell configurations.

ENHANCING THE PHYSICAL CHARACTERISTICS AND SHELF LIFE OF RICE WATER (ORYZA SATIVA L.) GEL SHAMPOO: THE ROLE OF PROPYLENE GLYCOL CONCENTRATION

Objective: This study aims to determine the effect of variations in propylene glycol concentration on the physical properties and physical stability of rice water shampoo gel and the physical strength of rice water shampoo gel preparation. Methods: The active ingredient was rice water, which was prepared by washing rice with water at the proportion of 1:1. The rice water then was applied to HPMC K4M-based gel preparations and further designed to prepare shampoo gel with varying concentrations of propylene glycol 8%, 10%, and 12%. The rice water shampoo gel was evaluated for its organoleptic properties, pH, viscosity, spreadability, flowability test, softness test, and physical stability test. Results: The results of the physical properties test on the three formulas produced an organoleptic clear pale yellow color, lemon odor, and semi-solid shape. The pH test results obtained by F1 was 7.24±0.08, F2 was 5.89±0.01, and F3 was 6.02±0.01. The viscosity test results obtained by F1 amounted to 2082±80.07 cP, F2 amounted to 4987±883.88 cP, and F3 amounted to 6531±232.79 cP. The foam height test results obtained by F1 amounted to 4.6±2.11 cm, F2 amounted to 1.46±0.05 cm, and F3 amounted to 1.7±0.17 cm. Variations in propylene glycol levels affect the physical properties of shampoo gel preparations at pH, viscosity, and foam height but do not affect the organoleptic practice. The stability test that was conducted at weeks 0, 1, 2, 3, and 4 with varying levels of propylene glycol showed significant changes on viscosity and foam height stability. Conclusion: Variations in the concentrations of propylene glycol in the three formulations affect the physical but have no effects on the pH stability of the rice water shampoo gel preparation.

Confounding effects of seasonality and anthropogenic river regulation on suspended particulate matter-driven mercury transport to coastal seas

Riverine mercury (Hg) is mainly transported to coastal areas in suspended particulate matter (SPM)-bound form, posing a potential threat to human health. Water discharge and SPM characteristics in rivers vary naturally with seasonality and can also be arbitrarily disrupted by anthropogenic regulation events, but their effects on Hg transport remain unresolved. Aiming to understand the confounding effects of seasonality and anthropogenic river regulation on Hg and SPM transport, this study selected the highly sediment-laden Yellow River as a representative conduit. Significant variations in SPM concentrations (108 − 7097 mg/L) resulted in fluctuations in total mercury (THg, 3.79 − 111 ng/L) in river water corresponding to seasonality and anthropogenic water/sediment regulation. Principal component analysis and structural equation model revealed that SPM was the essential factor controlling THg and particulate Hg (PHg) in river water. While SPM exhibited equilibrium state in the dry season, a net resuspension during the anthropogenic regulation and net deposition in the wet season demonstrated the impact of SPM dynamics on Hg distribution and transport to coastal regions. Combining water discharge, SPM, and Hg concentrations, a modified model was developed to quantify Hg flux (2256 kg), over 98% of which was in particulate phase.

Effects of Inorganic Salts on the Phase Separation of Partially Miscible Solutes.

Partially miscible solutions with a lower critical solution temperature have promising applications in the field of physical chemistry. To better guide the utilization of these solutions in practice, we conduct an in-depth study about the phase separation behavior of the solution added with inorganic salts. The addition of the inorganic salts into the solution is found to consequently reduce the phase separation temperature. The variation of concentrations of inorganic salts does not notably affect the mass fraction of the separation. Moreover, the addition of inorganic salts in the solutions at lower mass fractions improves the separation mass fraction, while the addition of inorganic salts decreases the separation mass fraction at the mass fractions above 30%. It sheds light on selecting the proper mass fractions and inorganic salt concentrations. Furthermore, we explore the phase separation behavior of mixed solutions under different inorganic salt additions by means of a high-speed camera. The phase separation behavior under different inorganic salt systems shows a similar trend. However, calcium ions and Fe3+ ions in the solutions can greatly decrease the rate of droplet coalescence and result in an increase in phase separation. For better regulating the solutions with a lower critical solution temperature through inorganic salts, sodium chloride or potassium chloride is recommended with an appropriate concentration.

Physiologically Based Pharmacokinetic Modeling to Unravel the Drug-gene Interactions of Venlafaxine: Based on Activity Score-dependent Metabolism by CYP2D6 and CYP2C19 Polymorphisms.

Venlafaxine (VEN) is a commonly utilized medication for alleviating depression and anxiety disorders. The presence of genetic polymorphisms gives rise to considerable variations in plasma concentrations across different phenotypes. This divergence in phenotypic responses leads to notable differences in both the efficacy and tolerance of the drug. A physiologically based pharmacokinetic (PBPK) model for VEN and its metabolite O-desmethylvenlafaxine (ODV) to predict the impact of CYP2D6 and CYP2C19 gene polymorphisms on VEN pharmacokinetics (PK). The parent-metabolite PBPK models for VEN and ODV were developed using PK-Sim® and MoBi®. Leveraging prior research, derived and implemented CYP2D6 and CYP2C19 activity score (AS)-dependent metabolism to simulate exposure in the drug-gene interactions (DGIs) scenarios. The model's performance was evaluated by comparing predicted and observed values of plasma concentration-time (PCT) curves and PK parameters values. In the base models, 91.1%, 94.8%, and 94.6% of the predicted plasma concentrations for VEN, ODV, and VEN + ODV, respectively, fell within a twofold error range of the corresponding observed concentrations. For DGI scenarios, these values were 81.4% and 85% for VEN and ODV, respectively. Comparing CYP2D6 AS = 2 (normal metabolizers, NM) populations to AS = 0 (poor metabolizers, PM), 0.25, 0.5, 0.75, 1.0 (intermediate metabolizers, IM), 1.25, 1.5 (NM), and 3.0 (ultrarapid metabolizers, UM) populations in CYP2C19 AS = 2.0 group, the predicted DGI AUC0-96h ratios for VEN were 3.65, 3.09, 2.60, 2.18, 1.84, 1.56, 1.34, 0.61, and for ODV, they were 0.17, 0.35, 0.51, 0.64, 0.75, 0.83, 0.90, 1.11, and the results were similar in other CYP2C19 groups. It should be noted that PK differences in CYP2C19 phenotypes were not similar across different CYP2D6 groups. In clinical practice, the impact of genotyping on the in vivo disposition process of VEN should be considered to ensure the safety and efficacy of treatment.

Groundwater quality assessment using water quality index and principal component analysis in the Achnera block, Agra district, Uttar Pradesh, Northern India

The qualitative and quantitative assessment of groundwater is one of the important aspects for determining the suitability of potable water. Therefore, the present study has been performed to evaluate the groundwater quality for Achhnera block in the city of Taj, Agra, India, where groundwater is an important water resource. The groundwater samples, 50 in number were collected and analyzed for major ions along with some important trace element. This study has further investigated for the applicability of groundwater quality index (GWQI), and the principal component analysis (PCA) to mark out the major geochemical solutes responsible for origin and release of geochemical solutes into the groundwater. The results confirm that, majority of the collected groundwater samples were alkaline in nature. The variation of concentration of anions in collected groundwater samples were varied in the sequence as, HCO3− > Cl− > SO42− > F− while in contrast the sequence of cations in the groundwater as Na > Ca > Mg > K. The Piper diagram demonstrated the major hydro chemical facies which were found in groundwater (sodium bicarbonate or calcium chloride type). The plot of Schoellar diagram reconfirmed that the major cations were Na+ and Ca2+ ions, while in contrast; major anions were bicarbonates and chloride. The results showed water quality index mostly ranged between 105 and 185, hence, the study area fell in the category of unsuitable for drinking purpose category. The PCA showed pH, Na+, Ca2+, HCO3− and fluoride with strong loading, which pointed out geogenic source of fluoride contamination. Therefore, it was inferred that the groundwater of the contaminated areas must be treated and made potable before consumption. The outcomes of the present study will be helpful for the regulatory boards and policymaker for defining the actual impact and remediation goal.

Investigation of sensing performances of thick-film resistors sintered on fluorophlogopite glass-ceramic substrates

Strain gauges made of thick-film resistors (TFRs) offer a promising solution for monitoring the structural health of civil engineering projects, owing to their exceptional durability and stability. To address the issue of strain mismatch between alumina substrates and concrete materials, fluorophlogopite glass-ceramic (FGC), which has a low elastic modulus and good machinability, was utilized as a substrate for thick-film resistors. Compatibility between resistor pastes and substrates should be considered, as the substrate can significantly impact the performance of thick-film resistors. TFRs with varying concentrations of RuO2 were screen printed and fired at different temperatures on both FGC and 96% Al2O3 (as a control) substrates. The sheet resistivity, temperature coefficient of resistance (TCR), and gauge factor (GF) of TFRs were evaluated. The strain sensing performances of TFRs on FGC evaluated under compressive loading. The results indicate that TFRs on FGC substrates exhibit higher hot TCRs, lower sheet resistivities, and lower gauge factors than those on Al2O3 substrates under typical firing conditions of 850°C for 10 minutes. As the concentration of RuO2 increases, the differences in properties between TFRs on the two substrates become less pronounced. Microstructural analyses of the interface between the resistor layer and substrate indicate that changes in properties of TFRs on FGC substrates are primarily attributed to variations in RuO2 concentration and glass phase composition, which result from interactions between the resistor layer and FGC substrate. The interaction between the FGC substrate and the resistor layer can be significantly alleviated by lowering the firing temperature from 850°C to 750°C, resulting in TFRs on FGC substrates with properties comparable to those on alumina substrates. The TFRs on FGC exhibit favorable sensitivity, linearity, repeatability, and hysteresis within the input compression strain range of 0–3043με, thus showcasing significant potential for application in structural health monitoring (SHM) in civil engineering.

Numerical Simulation of the Smoke Distribution Characteristics in a T-Shaped Roadway

This paper numerically analyzes the influence of heat release rate (HRR) and longitudinal ventilation velocity on smoke distribution characteristics in a T-shaped roadway when the fire source was located upstream of the T-junction. The back-layering length, critical ventilation velocity, smoke temperature, and CO concentration in the main and branched roadway were investigated and analyzed. The results showed that the ventilation velocity is the key factor influencing back-layering length, while the effect of HRR on back-layering length is gradually weakened as HRR increases. The critical ventilation velocity in the T-shaped roadway is higher than in a single-tube roadway, and the predicted model of dimensional critical ventilation velocity in a T-shaped bifurcated roadway is proposed. The correlation between average temperature (Z = 1.6 m) (both in the main roadway I and the branched roadway) and ventilation velocity fits the power function, and the variation in average temperature (Z = 1.6 m) according to HRR fits the linear formula. The relation between average concentration of CO (Z = 1.6 m) (both inside the main roadway I and the branched roadway) and longitudinal ventilation velocity follows the power relation, and the variation in average concentration of CO (Z = 1.6 m) according to HRR follows the linear function.

The impacts of different seasons on macroinvertebrate community structure and functional diversity in the Jingui River, China

Species diversity and functional diversity indices reflect the variations of community structure and their responses to environmental stress. This study clarifies the biodiversity of macroinvertebrates in the Jingui River of Shenzhen and their response to the aquatic environment across different seasons. Utilizing ten functional traits, we conducted a seasonal survey at ten sampling points to examine changes in macroinvertebrate species and functional diversity, along with their relationships with environmental variables in the JGR. The results of the study demonstrated that the species and functional diversity of macroinvertebrates were significantly affected by season. Redundancy analysis indicated that water velocity, hydrogen ion concentration, turbidity, water depth, and total nitrogen were the most important factors for macroinvertebrate functional diversity. Moreover, structural equation modeling demonstrated that seasonal variations in total nitrogen, ammonia nitrogen, nitrate nitrogen, conductivity, water velocity, and hydrogen ion concentration directly or indirectly impacted the species diversity and functional diversity of macroinvertebrates. This study provides valuable information into the seasonal variation characteristics of macroinvertebrate communities, contributing to a more comprehensive understanding of biodiversity crucial for maintaining river ecosystem functioning and stability.

Antibacterial Activity Test of Ag Nanoparticle Soap Turi Leaf Extract (Sesbania grandiflora) Against Staphylococcus aureus Bacteria

Silver metal nanoparticles have biocidal properties as a non-toxic inorganic antibacterial agent. Flavonoid compounds from the Turi Leaf (Sesbania grandiflora) plant extract as a natural bio reductant. In the research, UV-Vis spectrophotometric testing parameters and antibacterial soap preparations were carried out. Synthesis and Characteristics of Silver Nanoparticles were carried out, with soap preparations made into 3 formulas with different variations in Agno3 concentration, namely 1 mM, 2 mM, and 3 mM and antibacterial activity tested using well diffusion. The results of the absorption spectrum of Ag nanoparticles show the highest peak at a value of 280-290 nm. In the antibacterial activity test, the greater the AgNO3 concentration in the Ag nanoparticle soap, the greater the inhibition zone created by Staphylococcus aureus bacteria. The optimum concentration in soap preparations is 3 mM producing a wavelength of 300 nm with an absorbance value of 1.105. In terms of antibacterial activity, the most effective way to inhibit the growth of Staphylococcus aureus bacteria is formulation III, with an inhibitory power of 7.69 cm2 .

Lactate/Hydroxycarboxylic Acid Receptor 1 in Alzheimer's Disease: Mechanisms and Therapeutic Implications-Exercise Perspective.

Lactate has a novel function different from previously known functions despite its traditional association with hypoxia in skeletal muscle. It plays various direct and indirect physiological functions. It is a vital energy source within the central nervous system (CNS) and a signal transmitter regulating crucial processes, such as angiogenesis and inflammation. Activating lactate and its associated receptors elicits effects like synaptic plasticity and angiogenesis alterations. These effects can significantly influence the astrocyte-neuron lactate shuttle, potentially impacting cognitive performance. Decreased cognitive function relates to different neurodegenerative conditions, including Alzheimer's disease (AD), ischemic brain injury, and frontotemporal dementia. Therefore, lactic acid has significant potential for treating neurodegenerative disorders. Exercise is a method that induces the production of lactic acid, which is similar to the effect of lactate injections. It is a harmless and natural way to achieve comparable results. Animal experiments demonstrate that high-intensity intermittent exercise can increase vascular endothelial growth factor (VEGF) levels, thus promoting angiogenesis. In vivo, lactate receptor-hydroxycarboxylic acid receptor 1 (HCAR1) activation can occur by various stimuli, including variations in ion concentrations, cyclic adenosine monophosphate (cAMP) level elevations, and fluctuations in the availability of energy substrates. While several articles have been published on the benefits of physical activity on developing Alzheimer's disease in the CNS, could lactic acid act as a bridge? Understanding how HCAR1 responds to these signals and initiates associated pathways remains incomplete. This review comprehensively analyzes lactate-induced signaling pathways, investigating their influence on neuroinflammation, neurodegeneration, and cognitive decline. Consequently, this study describes the unique role of lactate in the progression of Alzheimer's disease.