Predicting micropollutant removal through nanopore-sized membranes using several machine-learning approaches based on feature engineering.

Predicting the efficacy of micropollutant separation through functionalized membranes is an arduous endeavor. The challenge stems from the complex interactions between the physicochemical properties of the micropollutants and the basic principles underlying membrane filtration. This study aimed to compare the effectiveness of a modest dataset on various machine learning tools (ML) tools in predicting micropollutant removal efficiency for functionalized reverse osmosis (RO) and nanofiltration (NF) membranes. The inherent attributes of both the micropollutants and the membranes are utilized as input factors. The chosen ML tools are supervised algorithm (adaptive network-based fuzzy inference system (NF), linear regression framework (linear regression (LR)), stepwise linear regression (SLR) and multivariate linear regression (MVR)), and unsupervised algorithm (support vector machine (SVM) and ensemble boosted tree (BT)). The feature engineering and parametric dependency analysis revealed that characteristics of micropollutants, such as maximum projection diameter (MaxP), minimal projection diameter (MinP), molecular weight (MW), and compound size (CS), exhibited a notably positive impact on the correlation with removal efficiency. Model combination with key variables demonstrated high prediction accuracy in both supervised and unsupervised ML for micropollutant removal efficiency. An NF-grid partitioning (NF-GP) model achieved the highest accuracy with an R 2 value of 0.965, accompanied by low error metrics, specifically an RMSE and MAE of 3.65. It is owed to the handling of the complex spatial and temporal aspects of micropollutant data through division into consistent subsets facilitating improved identification of rejection efficiency and relationships. The inclusion of inputs with both negative and positive correlations introduces variability, amplifies the system responsiveness, and impedes the precision of predictive models. This study identified key micropollutant properties, including MaxP, MinP, MW, and CS, as crucial factors for efficient micropollutant rejection during real-time filtration applications. It also allowed the design of pore size of self-prepared membranes for the enhanced separation of micropollutants from wastewater.

Enhancing Li+ recovery in brine mining: integrating next-gen emotional AI and explainable ML to predict adsorption energy in crown ether-based hierarchical nanomaterials.

Artificial intelligence (AI) is being employed in brine mining to enhance the extraction of lithium, vital for the manufacturing of lithium-ion batteries, through improved recovery efficiencies and the reduction of energy consumption. An innovative approach was proposed combining Emotional Neural Networks (ENN) and Random Forest (RF) algorithms to elucidate the adsorption energy (AE) (kcal mol-1) of Li+ ions by utilizing crown ether (CE)-incorporated honeycomb 2D nanomaterials. The screening and feature engineering analysis of honeycomb-patterned 2D materials and individual CE were conducted through Density Functional Theory (DFT) and Gaussian 16 simulations. The selected honeycomb-patterned 2D materials encompass graphene, silicene, and hexagonal boron nitride, while the specific CEs evaluated are 15-crown-5 and 18-crown-6. The crown-passivated 2D surfaces held a significant adsorption site through van der Waals forces for efficient recovery of Li+ ions. ENN predicted the targeted adsorption sites with high precision and minimal deviation. The eTAI (XAI) based Shapley Additive exPlanations (SHAP) was also explored for insight into the feature importance of CE embedded 2D nanomaterials for the recovery of Li+ ions. The extreme gradient boosting algorithm (XGBoost) model demonstrated a RT-2-MAPE = 0.4618% and ENN-2-MAPE = 0.4839% for the feature engineering analysis. This research would be an insight into the AI-driven nanotechnology that presents a viable and sustainable approach for the extraction of natural resources through the application of brine mining.

Surface water quality index forecasting using multivariate complementing approach reinforced with locally weighted linear regression model.

River water quality management and monitoring are essential responsibilities for communities near rivers. Government decision-makers should monitor important quality factors like temperature, dissolved oxygen (DO), pH, and biochemical oxygen demand (BOD). Among water quality parameters, the BOD throughout 5 days is an important index that must be detected by devoting a significant amount of time and effort, which is a source of significant concern in both academic and commercial settings. The traditional experimental and statistical methods cannot give enough accuracy or solve the problem for a long time to detect something. This study used a unique hybrid model called MVMD-LWLR, which introduced an innovative method for forecasting BOD in the Klang River, Malaysia. The hybrid model combines a locally weighted linear regression (LWLR) model with a wavelet-based kernel function, along with multivariate variational mode decomposition (MVMD) for the decomposition of input variables. In addition, categorical boosting (Catboost) feature selection was used to discover and extract significant input variables. This combination of MVMD-LWLR and Catboost is the first use of such a complete model for predicting BOD levels in the given river environment. In addition, an optimization process was used to improve the performance of the model. This process utilized the gradient-based optimization (GBO) approach to fine-tune the parameters and better the overall accuracy of predicting BOD levels. To assess the robustness of the proposed method, we compared it to other popular models such as kernel ridge (KRidge) regression, LASSO, elastic net, and gaussian process regression (GPR). Several metrics, comprising root-mean-square error (RMSE), R (correlation coefficient), U95% (uncertainty coefficient at 95% level), and NSE (Nash-Sutcliffe efficiency), as well as visual interpretation, were used to evaluate the predictive efficacy of hybrid models. Extensive testing revealed that, in forecasting the BOD parameter, the MVMD-LWLR model outperformed its competitors. Consequently, for BOD forecasting, the suggested MVMD-LWLR optimized with the GBO algorithm yields encouraging and reliable results, with increased forecasting accuracy and minimal error.

Insight into soft chemometric computational learning for modelling oily-wastewater separation efficiency and permeate flux of polypyrrole-decorated ceramic-polymeric membranes.

Reliable modeling of oily wastewater emphasizes the paramount importance of sustainable and health-conscious wastewater management practices, which directly aligns with the Sustainable Development Goals (SDG) while also meeting the guidelines of the World Health Organization (WHO). This research explores the efficiency of utilizing polypyrrole-coated ceramic-polymeric membranes to model oily wastewater separation efficiency (SE) and permeate flux (PF) based on established experimental procedures. In this area, computational simulation still needs to be explored. The study developed predictive regression models, including robust linear regression (RLR), stepwise linear regression (SWR) and linear regression (LR) for the ceramic-polymeric porous membrane, aiming to interpret its complex performance across diverse conditions and, thus, develop its utility in oily wastewater treatment applications. Subsequently, a novel, simple average ensemble paradigm was explored to reduce errors and improve prediction skills. Prior to the development of the model, stability and reliability analysis of the data was conducted based on Philip Perron tests with the Bartlett kernel estimation method. The accuracy of the SE exhibited a high consistency, averaging 99.92% with minimal variability (standard deviation of 0.026%), potentially simplifying its prediction compared to PF. The modes were validated and evaluated using metrics like MAE, RMSE, Speed, and MSE, in addition to 2D graphical and cumulative distribution function graphs. The LR model emerged as the best with the lowest RMSE =0.21951, indicating superior prediction accuracy, followed closely by RLR with an RMSE = 0.22359. SWLR, while having the highest RMSE = 0.34573, marked its dominance in prediction speed with 110 observations per second. Notably, the RLR model justified a reduction in error by approximately 35.29% compared to SWLR. Moreover, the training efficiency of the LR model exceeded, demanding a mere 2.9252 s, marking a reduction of about 32.54% compared to SWLR. The improved simple ensemble learning proved merit over the three models regarding error accuracy. This study emphasizes the essential role of soft-computing learning in optimizing the design and performance of ceramic-polymeric membranes.

Design and Machine Learning Prediction of In Situ Grown PDA-Stabilized MOF (UiO-66-NH2) Membrane for Low-Pressure Separation of Emulsified Oily Wastewater.

Significant progress has been made in designing advanced membranes; however, persistent challenges remain due to their reduced permeation rates and a propensity for substantial fouling. These factors continue to pose significant barriers to the effective utilization of membranes in the separation of oil-in-water emulsions. Metal-organic frameworks (MOFs) are considered promising materials for such applications; however, they encounter three key challenges when applied to the separation of oil from water: (a) lack of water stability; (b) difficulty in producing defect-free membranes; and (c) unresolved issue of stabilizing the MOF separating layer on the ceramic membrane (CM) support. In this study, a defect-free hydrolytically stable zirconium-based MOF separating layer was formed through a two-step method: first, by in situ growth of UiO-66-NH2 MOF into the voids of polydopamine (PDA)-functionalized CM during the solvothermal process, and then by facilitating the self-assembly of UiO-66-NH2 with PDA using a pressurized dead-end assembly. A stable MOF separating layer was attained by enriching the ceramic support with amines and hydroxyl groups using PDA, which assisted in the assembly and stabilization of UiO-66-NH2. The PDA-s-UiO-66-NH2-CM membrane displayed air superhydrophilicity and underwater superoleophobicity, demonstrating its oil resistance and high antifouling behavior. The PDA-s-UiO-66-NH2-CM membrane has shown exceptionally high permeability and separation capacity for challenging oil-in-water emulsions. This is attributed to numerous nanochannels from the membrane and its high resistance to oil adhesion. The membranes showed excellent stability over 15 continuous test cycles, which indicates that the developed MOFs separating layers have a low tendency to be clogged by oil droplets during separation. Machine learning-based Gaussian process regression (GPR) models as nonparametric kernel-based probabilistic models were employed to predict the performance efficiency of the PDA-s-UiO-66-NH2-CM membrane in oil-in-water separation. The outcomes were compared with the support vector machine (SVM) and decision tree (DT) algorithm. This efficiency includes various metrics related to its separation accuracy, and the models were developed through feature engineering to identify and utilize the most significant factors affecting the membrane's performance. The results proved the reliability of GPR optimization with the highest prediction accuracy in the validation phase. The average percentage increase of the GPR model compared to the SVM and DT model was 6.11 and 42.94%, respectively.

Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study.

Accurate and reliable estimation of Reference Evapotranspiration (ETo) is crucial for water resources management, hydrological processes, and agricultural production. The FAO-56 Penman-Monteith (FAO-56PM) approach is recommended as the standard model for ETo estimation; nevertheless, the absence of comprehensive meteorological variables at many global locations frequently restricts its implementation. This study compares shallow learning (SL) and deep learning (DL) models for estimating daily ETo against the FAO-56PM approach based on various statistic metrics and graphic tool over a coastal Red Sea region, Sudan. A novel approach of the SL model, the Catboost Regressor (CBR) and three DL models: 1D-Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were adopted and coupled with a semi-supervised pseudo-labeling (PL) technique. Six scenarios were developed regarding different input combinations of meteorological variables such as air temperature (Tmin, Tmax, and Tmean), wind speed (U2), relative humidity (RH), sunshine hours duration (SSH), net radiation (Rn), and saturation vapor pressure deficit (es-ea). The results showed that the PL technique reduced the systematic error of SL and DL models during training for all the scenarios. The input combination of Tmin, Tmax, Tmean, and RH reflected higher performance than other combinations for all employed models. The CBR-PL model demonstrated good generalization abilities to predict daily ETo and was the overall superior model in the testing phase according to prediction accuracy, stability analysis, and less computation cost compared to DL models. Thus, the relatively simple CBR-PL model is highly recommended as a promising tool for predicting daily ETo in coastal regions worldwide which have limited climate data.

Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm.

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies.

Presence of SARS-CoV-2-like coronaviruses in bats from east coast Malaysia.

Most of the public health importance coronaviruses, such as Severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV) and SARS-CoV-2 are likely originated from bats and spread to humans through intermediate hosts; civet cats, dromedary camel and Malayan pangolin, respectively. SARS-CoV-2-like coronaviruses were detected in Thailand, which is neighbouring with Kelantan in East Coast Malaysia. To date, there is no report on the presence of public health concerns (SARS-CoV, SARS-CoV-2 and MERS-CoV) coronaviruses in bats from Malaysia. This study was aimed to elucidate the presence of these coronaviruses in bat samples from East Coast, Malaysia. A total of hundred seventy oropharyngeal swab samples were collected from three states of East Coast Malaysia. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) was conducted based on partial 3' Untranslated region (3'UTR) or ORF10 gene and the products were sequenced. The sequences were compared with all coronavirus sequences from the National Center for Biotechnology Information-GenBank (NCBI-GenBank) using NCBI-Basic Local Alignment Search Tool (NCBI-BLAST) software. A phylogenetic tree was constructed to determine the genetic relationship among the detected coronaviruses with the reference coronaviruses from the NCBI-GenBank. Our results showed that SARSCoV-2-like viruses were present in 3% (5/170) of the bats from East Coast Malaysia that have 98-99% sequence identities and are genetically related to SARS-CoV-2 from humans. This finding indicates the presence of SARS-CoV-2-like viruses in bats from East Coast Malaysia that may become a public health concern in the future.

Experimental Investigation of a Plate-Frame Water Gap Membrane Distillation System for Seawater Desalination.

This study presented a detailed investigation into the performance of a plate-frame water gap membrane distillation (WGMD) system for the desalination of untreated real seawater. One approach to improving the performance of WGMD is through the proper selection of cooling plate material, which plays a vital role in enhancing the gap vapor condensation process. Hence, the influence of different cooling plate materials was examined and discussed. Furthermore, two different hydrophobic micro-porous polymeric membranes of similar mean pore sizes were utilized in the study. The influence of key operating parameters, including the feed water temperature and flow rate, was examined against the system vapor flux and gained output ratio (GOR). In addition, the used membranes were characterized by means of different techniques in terms of surface morphology, liquid entry pressure, water contact angle, pore size distribution, and porosity. Findings revealed that, at all conditions, the PTFE membrane exhibits superior vapor flux and energy efficiency (GOR), with 9.36% to 14.36% higher flux at a 0.6 to 1.2 L/min feed flow rate when compared to the PVDF membrane. The copper plate, which has the highest thermal conductivity, attained the highest vapor flux, while the acrylic plate, which has an extra-low thermal conductivity, recorded the lowest vapor flux. The increasing order of GOR values for different cooling plates is acrylic < HDPE < copper < aluminum < brass < stainless steel. Results also indicated that increasing the feed temperature increases the vapor flux almost exponentially to a maximum flux value of 30.36 kg/m2hr. The system GOR also improves in a decreasing pattern to a maximum value of 0.4049. Moreover, a long-term test showed that the PTFE membrane, which exhibits superior hydrophobicity, registered better salt rejection stability. The use of copper as a cooling plate material for better system performance is recommended, while cooling plate materials with very low thermal conductivities, such as a low thermally conducting polymer, are discouraged.

Presence of SARS-CoV-2-like coronaviruses in bats from east coast Malaysia

@#Most of the public health importance coronaviruses, such as Severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV) and SARS-CoV-2 are likely originated from bats and spread to humans through intermediate hosts; civet cats, dromedary camel and Malayan pangolin, respectively. SARS-CoV-2-like coronaviruses were detected in Thailand, which is neighbouring with Kelantan in East Coast Malaysia. To date, there is no report on the presence of public health concerns (SARS-CoV, SARS-CoV-2 and MERS-CoV) coronaviruses in bats from Malaysia. This study was aimed to elucidate the presence of these coronaviruses in bat samples from East Coast, Malaysia. A total of hundred seventy oropharyngeal swab samples were collected from three states of East Coast Malaysia. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) was conducted based on partial 3’ Untranslated region (3’UTR) or ORF10 gene and the products were sequenced. The sequences were compared with all coronavirus sequences from the National Center for Biotechnology Information-GenBank (NCBI-GenBank) using NCBI-Basic Local Alignment Search Tool (NCBI-BLAST) software. A phylogenetic tree was constructed to determine the genetic relationship among the detected coronaviruses with the reference coronaviruses from the NCBI-GenBank. Our results showed that SARSCoV-2-like viruses were present in 3% (5/170) of the bats from East Coast Malaysia that have 98-99% sequence identities and are genetically related to SARS-CoV-2 from humans. This finding indicates the presence of SARS-CoV-2-like viruses in bats from East Coast Malaysia that may become a public health concern in the future.

Kolaviron pre-treatment suppresses 7, 12 dimethylbenzanthracene-induced alterations in estrogen receptor-α, CYP 1A1, oxidative stress and inflammation in female Wistar rats.

Due to the need to develop locally available, cheaper, and efficacious treatment regimens for breast cancer, the chemopreventive effect of kolaviron (KV), an extract of Garcinia kola seeds was examined. Fifty (50) female Wistar rats (120-180 g) were assigned to five groups (control group, 7, 12 dimethylbenzanthracene [DMBA] groups, tamoxifen group) of 10 rats each. They were pre-treated with KV thrice a week for four weeks except control. Estrogen receptor-α (ER-α) levels were determined in the pre-treated rats before induction of mammary carcinogenesis. After the four weeks pre-treatment period, 80 mg/kg of DMBA was used for induction. A hundred and fifty (150) days after induction, the rats were sacrificed humanely. Significantly higher levels of ER-α, formation of lobular neoplastic cells, epithelial hyperplasia, lymphocyte infiltration, increased cytokines (interleukin-6 [IL-6] and tumor necrosis factor-α [TNF-α]), CYP1A1 activity and malondialdehyde (MDA) with a corresponding decrease in superoxide dismutase (SOD), catalase and glutathione peroxidase were observed in DMBA-induced rats. Pre-treatment with KV at 200 mg/kg body weight significantly (p < .05) decreased ER-α levels by 19.01% and 37.52%, [IL-6] by 36.37% and 20.55%, TNF-α by 42.2% and 12.33% in serum and mammary tissue respectively. Also, a significant (p < .05) decrease in serum CYP1A1 activity, MDA with concomitant increase in SOD, catalase and glutathione peroxidase activities were observed in serum and mammary tissue respectively. Collectively, the results suggest that KV could be further explored in targeting chemoprevention of DMBA-induced mammary damage. PRACTICAL APPLICATIONS: Garcinia kola is widely cultivated in West and Central Africa with kolaviron (KV) as its major constituents. The seeds which have a bitter astringent taste are widely consumed by people in the region. Locals claim that consumption of the seeds provides relief for the management of several ailments including cancer. However, scientific investigations that provide a basis for these claims are still needed. This study provides evidence that points to the ameliorative potential of KV on breast cancer model. The results will be beneficial to local communities who hitherto had no knowledge on the potential of G. kola in chemoprevention. The results from this study will also attract further research attention from the international scientific community to examine the anti-cancer benefits of G. kola. This will also be beneficial to the global community due to the increasing number of breast cancer cases recorded annually.

Selective activation of central thalamic fiber pathway facilitates behavioral performance in healthy non-human primates.

Central thalamic deep brain stimulation (CT-DBS) is an investigational therapy to treat enduring cognitive dysfunctions in structurally brain injured (SBI) patients. However, the mechanisms of CT-DBS that promote restoration of cognitive functions are unknown, and the heterogeneous etiology and recovery profiles of SBI patients contribute to variable outcomes when using conventional DBS strategies,which may result in off-target effects due to activation of multiple pathways. To disambiguate the effects of stimulation of two adjacent thalamic pathways, we modeled and experimentally compared conventional and novel 'field-shaping' methods of CT-DBS within the central thalamus of healthy non-human primates (NHP) as they performed visuomotor tasks. We show that selective activation of the medial dorsal thalamic tegmental tract (DTTm), but not of the adjacent centromedian-parafascicularis (CM-Pf) pathway, results in robust behavioral facilitation. Our predictive modeling approach in healthy NHPs directly informs ongoing and future clinical investigations of conventional and novel methods of CT-DBS for treating cognitive dysfunctions in SBI patients, for whom no therapy currently exists.

WNT10A gene is the second molecular candidate in a cohort of young Italian subjects with ectodermal derivative impairment (EDI).

Ectodermal dysplasias are a group of genetic disorders defined by ectodermal derivative impairment (EDI). To test the impact of the Wnt/beta-catenin pathway in the genetic screening of EDI, we performed a molecular gene study of WNT10A in 60 subjects from a population of 133 young Italian patients referred for the impairment of at least one major ectodermal-derived structure and who had a previous negative molecular screen for ectodysplasin signaling pathway genes ED1, EDAR, and EDARADD. Fourteen WNT10A mutations were identified in 33 subjects (24.8%), 11 of which were novel variants. The phenotype was evaluated through a detailed clinical examination of the major and minor ectodermal-derived structures. This study is the first to show that, after ED1, WNT10A is the second molecular candidate for EDI in a large Italian Caucasian population. The study confirmed that Phe228Ile is the most frequent WNT10A variant in Caucasian populations, and that WNT10A mutations are associated with large variability in EDI.

Phenotypic heterogeneity and mutational spectrum in a cohort of 45 Italian males subjects with X-linked ectodermal dysplasia.

Ectodermal dysplasias (EDs) are a group of genetic disorders characterized by the abnormal development of the ectodermal-derived structures. X-linked hypohidrotic ectodermal dysplasia, resulting from mutations in ED1 gene, is the most common form. The main purpose of this study was to characterize the phenotype spectrum in 45 males harboring ED1 mutations. The study showed that in addition to the involvement of the major ectodermal tissues, the majority of patients also have alterations of several minor ectodermal-derived structures. Characterizing the clinical spectrum resulting from ED1 gene mutations improves diagnosis and can direct clinical care.

Germinated brown rice and its bioactives modulate the activity of uterine cells in oophorectomised rats as evidenced by gross cytohistological and immunohistochemical changes.

BACKGROUND: Germinated brown rice (GBR) is gaining momentum in the area of biomedical research due to its increased use as a nutraceutical for the management of diseases. The effect of GBR on the reproductive organs of oophorectomised rats was studied using the gross, cytological, histological and immunohistochemical changes, with the aim of reducing atrophy and dryness of the genital organs in menopause. METHODS: Experimental rats were divided into eight groups of six rats per group. Groups 1, 2 and 3 (sham-operated (SH), oophorectomised without treatment (OVX) and oophorectomised treated with 0.2 mg/kg oestrogen, respectively) served as the controls. The groups 4,5,6,7 and 8 were treated with 20 mg/kg Remifemin, 200 mg/kg of GBR, ASG, oryzanol and GABA, respectively. All treatments were administered orally, once daily for 8 weeks. Vaginal smear cytology was done at the 7th week on all the rats. The weight and dimensions of the uterus and vagina were determined after sacrifice of the rats. Uterine and vaginal tissues were taken for histology and Immunohistochemical examinations. RESULTS: GBR and its bioactives treated groups significantly increased the weight and length of both the uterus and the vagina when compared to Oophorectomised non-treated group (OVX-non-treated) (p < 0.05). Significant changes were observed in the ratio of cornified epithelial cells and number of leucocytes in the vaginal cytology between the oophorectomised non-treated and treated groups. There was also an increase in the luminal and glandular epithelial cells activity in the treated compared with the untreated groups histologically. Immunohistochemical staining showed specific proliferating cell nuclear antigen (PCNA) in the luminal and glandular epithelium of the treated groups, which was absent in the OVX-non-treated group. GBR improved the length and weight of the uterus and also increased the number of glandular and luminal cells epithelia of the vagina. CONCLUSION: GBR and its bioactives could be a potential alternative in improving reproductive system atrophy, dryness and discomfort during menopause.