Use Of Principal Component Analysis Research Articles

Cultures of support and resilience are associated with certified pharmacy technicians embracing new roles.

Various technological, economic, and regulatory factors are creating opportunities for pharmacy technicians to take on additional responsibilities. Technicians in the broader sense have indicated a preference for expanded scope of duties; however, it is not known what drives technicians' greater inclinations to accept these new roles. The purpose of this study was to determine the association of various work-related factors, such as co-worker support, pharmacist-leadership style, future uncertainty, their own organizational commitment, and personal characteristics of technicians, including their resilience, on pharmacy technicians' willingness to take on emerging responsibilities in pharmacy. A self-administered questionnaire survey was disseminated through email to a national, randomized sample of 3000 technicians certified through the National Healthcareer Association (NHA). The questionnaire solicited data on willingness to participate in either of 13 emerging responsibilities as well as resilience, perceived transformative leadership behaviors of supervising pharmacists, future uncertainty, coworker support, organizational commitment, and various personal and practice-related variables. Following the use of principal component analyses for item purification and summation of various scale responses, inferential analyses were conducted via independent sample t-tests, one way analyses of variance, and Pearson's product moment correlation, as appropriate. From 2906 surveys delivered, 878 were returned, with 745 of them providing completed responses on willingness to participate in emerging responsibilities. Willingness on most items/responsibilities was highly rated, with many means being at least 4 on a 5-point scale, though some were lower and perhaps a reflection of less exposure to these by way of their personal experience orpractice setting. Technicians' resilience, their coworker support, organizational commitment, and perceived transformative behaviors undertaken by their supervising pharmacists were all highly associated with willingness to engage. Pharmacists, pharmacy technician peers, and organizations can contribute to a culture that is supportive for pharmacy technicians and may serve to encourage commitment, resilience, and willingness to embrace new, or emerging responsibilities.

Retrieval of Volcanic Ash Cloud Base Height Using Machine Learning Algorithms

There are distinct differences between radiation characteristics of volcanic ash and meteorological clouds, and conventional retrieval methods for cloud base height (CBH) of the latter are difficult to apply to volcanic ash without substantial parameterisation and model correction. Furthermore, existing CBH inversion methods have limitations, including the involvement of many empirical formulae and a dependence on the accuracy of upstream cloud products. A machine learning (ML) method was developed for the retrieval of volcanic ash cloud base height (VBH) to reduce uncertainties in physical CBH retrieval methods. This new methodology takes advantage of polar-orbit active remote-sensing data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), from vertical profile information and from geostationary passive remote-sensing measurements from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Advanced Geostationary Radiation Imager (AGRI) aboard the Meteosat Second Generation (MSG) and FengYun-4B (FY-4B) satellites, respectively. The methodology involves a statistics-based algorithm with hybrid use of principal component analysis (PCA) and one of four ML algorithms including the k-nearest neighbour (KNN), extreme gradient boosting (XGBoost), random forest (RF), and gradient boosting decision tree (GBDT) methods. Eruptions of the Eyjafjallajökull volcano (Iceland) during April-May 2010, the Puyehue-Cordón Caulle volcanic complex (Chilean Andes) in June 2011, and the Hunga Tonga-Hunga Ha’apai volcano (Tonga) in January 2022 were selected as typical cases for the construction of the training and validation sample sets. We demonstrate that a combination of PCA and GBDT performs more accurately than other combinations, with a mean absolute error (MAE) of 1.152 km, a root mean square error (RMSE) of 1.529 km, and a Pearson’s correlation coefficient (r) of 0.724. Use of PCA as an additional process before training reduces feature relevance between input predictors and improves algorithm accuracy. Although the ML algorithm performs well under relatively simple single-layer volcanic ash cloud conditions, it tends to overestimate VBH in multi-layer conditions, which is an unresolved problem in meteorological CBH retrieval.

Predicting Short-Term Deformation in the Central Valley Using Machine Learning

Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers’ integrity.

The use of principal component analysis for reduction of training load data in professional soccer

The aim of this study was to explore the use of principal component analysis (PCA) in understanding multivariate relationships in soccer training load data. Training load data were collected from 20 professional male soccer players during a 28-week in-season period. Twelve training load variables (total distance, PlayerLoadTM, low-speed running distance, moderate-speed running distance, high-speed running distance, sprint distance, moderate-speed running efforts, high-speed running efforts, sprint efforts, accelerations, decelerations, and changes of direction) were collected during training sessions, with correlation analysis revealing high intercorrelation between most variables (r = 0.04-0.98). Principal component analysis was performed on datasets containing all players and on individual players. On the whole dataset, two principal components were retained explaining a total of 81% of data variance. The first component comprised variables associated with distances in speed zones and the second component changes of direction. Whilst some individual variation existed among players, distances in speed zones were loaded on the first component and inertial movement analysis variables, such as accelerations, decelerations, and changes of direction, were loaded on the second component. These findings evidence the strong relationships between several common training load variables and highlight the risk of data redundancy. By selecting variables from each component, practitioners can reduce the number of variables reported whilst retaining as much of the variation in data as possible.

Development of an automated Raman system and use of principal component analysis to classify real and counterfeit liquors.

We developed an automated Raman measurement platform for the customized design of various solution containers. We used the software LabVIEW to integrate the entire automatic measurement process. By designing an intuitive human-machine interface, the user only needs to input a few setting parameters and can efficiently operate the machine in automation mode for an array of solutions containing real or counterfeit liquors such as kaoliang liquor, vodka, rum, gin, rice wine, ethanol, and methanol. In this study, data from various alcoholic beverage solutions were subjected to principal component analysis (PCA) to distinguish from the low-concentration counterfeit liquors (methanol <50 g L-1). Moreover, several brands of liquors with the same alcohol concentration were successfully classified into different groups based on a combination of Raman spectroscopy and PCA analysis.

Sample size prediction for anomaly detection in locks

Artificial intelligence in manufacturing systems is currently most used for quality control and predictive maintenance. In the lock industry, quality control of final assembled cylinder lock is still done by hand, wearing out the operators’ wrists and introducing subjectivity which negatively affects reliability. Studies have shown that quality control can be automated using machine-learning to analyse torque measurements from the locks. The resulting performance of the approach depends on the dimensionality and size of the training dataset but unfortunately, the process of gathering data can be expensive so the amount collected data should therefore be minimized with respect to an acceptable performance measure. The dimensionality can be reduced with a method called Principal Component Analysis and the training dataset size can be estimated by repeated testing of the algorithms with smaller datasets of different sizes, which then can be used to extrapolate the expected performance for larger datasets. The purpose of this study is to evaluate the state-of-the-art methods to predict and minimize the needed sample size for commonly used machine-learning algorithms to reach an acceptable anomaly detection accuracy using torque measurements from locks. The results show that the learning curve with the best fit to the training data does not always give the best predictions. Instead, performance depends on the amount of data used to create the curve and the particular machine-learning algorithm used. Overall, the exponential and power-law functions gave the most reliable predictions and the use of principal component analysis greatly reduced the learning effort for the machine-learning algorithms. With torque measurements from 50-150 locks, we predicted a detection accuracy of over 95% while the current method of using the human tactile sense gives only 16% accuracy.

Identification of cancer types from gene expressions using learning techniques

Tumor is the major cause of death all around the world in recent days. Early detection and prediction of a cancer type are important for a patient’s well-being. Functional genomic data has recently been used in the effective and early detection of cancer. According to previous research, the use of microarray data in cancer prediction has evidenced two main problems as high dimensionality and limited sample size. Several researchers have used numerous statistical and machine learning-based methods to classify cancer types but still, limitations are there which makes cancer classification a difficult job. Deep Learning (DL) and Convolutional Neural Networks (CNN) have been proven with effective analyses of unstructured data including gene expression data. In the proposed method gene expression data for five types of cancer is collected from The Cancer Genome Atlas (TCGA). Prominent features are selected using a hybrid Particle Swarm Optimization (PSO) and Random Forest (RF) algorithm followed by the use of Principal Component Analysis (PCA) for dimensionality reduction. Finally, for classification blend of Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory (Bi-LSTM) is used to predict the target type of cancer. Experimental results demonstrate that accuracy of the proposed method is 96.89%. As compared to existing work, our method outperformed with better results.

EigenCircuit: Divergent Synthetic Benchmark Generation for Hardware Security Using PCA and Linear Programming

Benchmarks are the standards by which technologies can be evaluated and fairly compared. In the field of digital circuits, benchmarks were critical for the development of CAD and FPGA tools decades ago. Hardware security is an emerging field of research where new techniques of security and vulnerability of hardware designs are being proposed in higher volume each year. Using decade-old VLSI/CAD-oriented benchmarks for analyzing the techniques has many issues as these benchmarks were not developed for security research. Additionally, the rise of statistical analysis or machine learning (ML) to model vulnerabilities and solve security issues demands a very large set of samples for training purposes. Since the number of available VLSI/CAD benchmarks is limited, such volume can only be obtained through synthetic benchmark generation tools. To accommodate both of these needs, the first hardware security-oriented synthetic circuit benchmark generation framework is developed in this article. With the use of principal component analysis (PCA) and linear optimization tool, the benchmarks generated by the proposed framework are “divergent,” that is having maximum variation in structures from each other. By accommodating user inputs for desired features, the framework offers customization for generating richer and more challenging benchmarks for data-driven hardware security. With thorough experimentation, we demonstrate our framework’s scalability, the structural and functional variations in the generated benchmarks, and the advantage of structurally variant synthetic benchmarks in hardware security applications.

Near-Infrared data classification at phone terminal based on the combination of PCA and CS-RBFSVC algorithms

Near-infrared (NIR) spectroscopy is a non-destructive, efficient and convenient detection technology, with the emergence of portable NIR spectrometers, NIR mobile applications (APPs) come into being. The popularity of intelligent mobile phones provides an impetus to the research and development of NIR APPs, however, the primary functions such as operating the NIR spectrometers and collecting data cannot satisfy NIR users in the field of data processing. Herein, we propose an APP processing NIR data locally at the mobile terminal, by the comprehensive utilization of Principal Component Analysis (PCA) and Cuckoo Search algorithm optimized Support Vector Classifier with radial basis function (RBFSVC) kernel (CS-RBFSVC). 738 NIR samples of four drugs (Cydiodine Buccal Tablets, Sulfasalazine Enteric-coated Tablets, Dexamethasone Acetate Tablets, Vecuronium Bromide for Injection) were used as the validation objects to train and test the data classification model. Firstly, the original data were subjected to dimensional reduction through PCA for the purpose of compressing calculation amount. Secondly, the CS-RBFSVC model was utilized to classify the types of drugs and their manufacturers, moreover, the improved accuracy and efficiency by introducing Cuckoo Search (CS) algorithm into RBFSVC were proven in comparison with the conventional grid optimized RBFSVC (Grid-RBFSVC) and Linear Support Vector Classifier (Linear-SVC). Last but not least, an APP based on the proposed PCA and CS-RBFSVC model is developed and demonstrated to be able to classify the type of drugs with an accuracy of 100%, the accuracies of classifying the drugs’ manufacturers were 100%, 100%, 98.3% and 90.7%, respectively. Conclusively, the proposed PCA and CS-RBFSVC based model can provide a low-consumption, high accuracy and quick strategy for NIR data classification and overcome the limitations of internal storage and operating speed at phone terminals, in conjunction with the portable NIR spectrometer, it is believed to push forward NIR technology into the instant detection and on-site inspection.

A decision fusion method based on classification models for water quality monitoring.

Monitoring of water quality is one of the world's main intentions for countries. Classification techniques based on support vector machines (SVMs) and artificial neural network (ANN) has been widely used in several applications of water research. Water quality assessment with high accuracy and efficiency with innovational approaches permitted us to acquire additional knowledge and information to obtain an intelligent monitoring system. In this paper, we present the use of principal component analysis (PCA) combined with SVM and ANN with decision templates combination data fusion method. PCA was used for features selection from original database. The multi-layer perceptron network (MLP) and the one-against-all strategy for SVM method have been widely used. Decision templates are applied to increase the accuracy of the water quality classification. The specific classification approach was employed to assess the water quality of the Tilesdit dam in Algeria as a study area, defined with a dataset of eight physicochemical parameters collected in the period 2009-2018, such as temperature, pH, electrical conductivity, and turbidity. The selection of the excellent parameters of the used models can be improving the performance of classification process. In order to assess their results, an experiment step using collected dataset corresponding to the accuracy and running time of training and test phases, and robustness to noise, is carried out. Various scenarios are examined in comparative study to obtain the most results of decision step with and without feature selection of the input data. From the results, we found that the integration of SVM and ANN with PCA yields accuracy up than 98%. The combination by decision templates of two classifiers SVM and ANN with PCA yields an accuracy of 99.24% using k-fold cross-validation. The combination data fusion enhanced expressively the results of the proposed monitoring framework that had proven a considerable ability in surface water quality assessment.

Food Insecurity in the Households of Polish Elderly: Diversity in the Perception of Its Causes by Demographic and Socioeconomic Characteristics

The aging of societies and the quality of life of the elderly may be accompanied by food insecurity. The aim of the study was to find the relationships between the perceptions of various causes of food insecurity, i.e., financial, social, health, and spatial, and then between those and the selected sociodemographic characteristics. The survey was conducted in late 2018 and early 2019 among 760 people aged 65 and older in two regions of Poland. Factor analysis with the use of principal component analysis (PCA) was used to identify the main causes of the prevalence of food insecurity. Cluster analysis using Ward’s hierarchical classification and logistic regression analysis were used to assess the relationship between the identified reasons for food insecurity, demographic characteristics, and socioeconomic status (SES). Two groups of causes that favor the experience of food insecurity among the elderly were identified, i.e., economic–social reasons and spatial–health reasons. They relate to such situations of food insecurity as concerns about food shortages, lack of staple foods, limited size or frequency of meals, and skipping meals. The high importance of economic–social (HE-S) reasons was associated with the low importance of spatial–health (LS-H) reasons, and conversely, the high importance of spatial–health (HS-H) causes was associated with the low importance of economic–social (LE-S) causes. HE-S and LS-H reasons were combined with low SES and residence in a city of more than 100,000 inhabitants. HS-H causes, on the other hand, were associated with LE-S causes and living in rural areas or towns of fewer than 100,000 inhabitants, as well as high SES. This specificity should be considered in the development of strategies and interventions aimed at reducing the phenomenon of experiencing food insecurity in the elderly population.

Machine learning techniques for prediction of multiple sclerosis progression

Patients afflicted by multiple sclerosis experience a relapsing-remitting course in about 85% of the cases. Furthermore, after a 10/15-year period their situation tends to worse, resulting in what is considered the second phase of multiple sclerosis. While treatments are now available to reduce the symptoms and slow down the progression of the disease, the administration of drugs must be adapted to the course of the disease, and predicting relapsing periods and the worsening of the symptoms can greatly improve the outcome of the treatment. For this reason, indicators such as the patient-reported outcome measures (PROMs) have been largely used to support early diagnosis and prediction of future relapsing periods in patients affected by multiple sclerosis. However, such indicators are insufficient, as the prediction they provide is often not accurate enough. In this paper, machine learning techniques have been applied to data obtained from clinical trial, in order to improve the prediction capabilities and provide doctors with an additional instrument to evaluate the clinical situation of patients. After the application of correlation indicators and the use of principal component analysis for the reduction of the dimensionality of the feature space, classification algorithms have been applied and compared, in order to identify the best suiting one for our purposes. After the application of re-balance algorithms, the accuracy of the machine learning-based prediction system reaches 79%, demonstrating the capability of the framework to correctly predict future progression of disability.

Effect of Governance on Macroeconomic Instability in Sub-Saharan Africa: Does Regional Analysis Matter?

Macroeconomic instability is a growth blockage in any contemporary societies, businesses, and governments. However, how to achieve macroeconomic stability remains a gap in extant literature for most developing countries. This study examines the effect of governance on macroeconomic instability in a sample of 42 Sub-Saharan African countries over the period 1996-2019. Four governance indicators, namely political governance, economic governance, institutional governance and general governance are constructed with the use of principal component analysis. Empirical evidence based on the Generalized Method of Moments and alternatively by Two-Stage least Squares technique for regional analysis reveals that the selected governance indicators do not have the same reducing influence on macroeconomic instability in Sub-Saharan Africa. Considering sub-regional analysis, evidence of a positive effect of political governance over growth instability in Central Africa is sparingly apparent in Voice and Accountability, and liberal democracy. This finding therefore recommend, improved local contents of governance policies toward economic sustainability, redesigning and returning the content of democracy in line with the demands, peculiarities and realities of central Africa state.

Application of Optimized Support Vector Machine Model in Tax Forecasting System

Tax forecast has an important impact on financial budget and tax plan. The amount of tax data is greatly increased, the difficulty of tax forecast is improved, and the accuracy is always difficult to keep up with the development demand. Analyze the application of optimized support vector machine model in tax prediction system. Based on the simple analysis of the research situation of tax prediction and the research status of data mining algorithm in tax data classification and prediction, this paper constructs a tax prediction model. Aiming at the problem of too many influencing factors of tax prediction, this paper puts forward the use of principal component analysis to extract the main factors, reduce the dimension of tax data, and reduce the difficulty of analysis. Support vector machine is used to realize tax prediction, aiming at the problem of parameter optimization proposed to optimize the parameters. Finally, the prediction accuracy is evaluated by comparing the error between tax prediction value and real value. The results show that the algorithm used in this paper can optimize the parameters of support vector machine. The tax prediction results show that the predicted value is similar to the real value curve. After grid search optimization, the introduction of principal component analysis reduces the redundancy and improves the prediction accuracy.

The Measurement and Influencing Factors of High-Quality Economic Development in China

From the perspective of economics, high-quality economic development is a concept that is not easy to grasp. How to quantify high-quality development is also a relatively complex topic. The combination of economic growth and development is high-quality economic development, reflecting the core connotation of the modern economic system. It is of great significance to measure the quality of economic development and study its influencing factors. Based on the new development concept of “innovation, coordination, green, openness, and sharing”, this paper establishes an evaluation index system for the high-quality development level of China’s economy. Then, the principal component analysis method was used to measure China’s high-quality economic development level and that of each province in China. Combined with high-quality development’s meaning and essential features, we can correctly judge the regional economy’s specific situation of high-quality development and analyze the results of high-quality economic development from the perspective of time series and spatial evolution. Based on this, we further explore the main factors that influence the level of high-quality economic development. Finally, some feasible suggestions are put forward to improve the quality of China’s economic development and promote the completion of economic transformation. The main contribution of this paper is that the use of principal component analysis can reduce the dimensional and order-of-magnitude differences between the indicators. In this way, we can better measure the high-quality development level of China’s economy, analyze its main influencing factors, and provide new possible paths for China’s economic transformation.

A Reinforcement Learning Application for Optimal Fluid and Vasopressor Interventions in Septic ICU Patients.

Sepsis is one of the leading causes of death in ICU and its timely recognition and management are of primary importance. Resuscitation from hypotension in patients with sepsis is one of the first challenges that require fluid and/or vasopressor administrations. Unfortunately, clinical guidelines provide only indications of the strategy that should be adopted in this critical population but personalized strategies are still missing. In this study, we propose a comparative analysis of reinforcement learning applications on ICU data collected in the electronic health records and publicly available within the MIMIC-III database. The ultimate goal of the study is to estimate the optimal fluid and vasopressor administrations. Results show that, after the use of principal component analysis for reducing feature space dimensionality, model performances increased, thus suggesting that additional preprocessing strategies might be used for both reducing the computational cost and refining model performances. Clinical relevance In a context where clinical guidelines are not able to provide the best treatment strategies at a patient level, reinforcement learning applications trained on retrospectively collected data may be used for developing models able to suggest to clinicians the optimal dosage of fluids and/or vasopressors in order to improve 90-day patients' survival.

Single trait versus principal component based association analysis for flowering related traits in pigeonpea

Pigeonpea, a tropical photosensitive crop, harbors significant diversity for days to flowering, but little is known about the genes that govern these differences. Our goal in the current study was to use genome wide association strategy to discover the loci that regulate days to flowering in pigeonpea. A single trait as well as a principal component based association study was conducted on a diverse collection of 142 pigeonpea lines for days to first and fifty percent of flowering over 3 years, besides plant height and number of seeds per pod. The analysis used seven association mapping models (GLM, MLM, MLMM, CMLM, EMLM, FarmCPU and SUPER) and further comparison revealed that FarmCPU is more robust in controlling both false positives and negatives as it incorporates multiple markers as covariates to eliminate confounding between testing marker and kinship. Cumulatively, a set of 22 SNPs were found to be associated with either days to first flowering (DOF), days to fifty percent flowering (DFF) or both, of which 15 were unique to trait based, 4 to PC based GWAS while 3 were shared by both. Because PC1 represents DOF, DFF and plant height (PH), four SNPs found associated to PC1 can be inferred as pleiotropic. A window of ± 2 kb of associated SNPs was aligned with available transcriptome data generated for transition from vegetative to reproductive phase in pigeonpea. Annotation analysis of these regions revealed presence of genes which might be involved in floral induction like Cytochrome p450 like Tata box binding protein, Auxin response factors, Pin like genes, F box protein, U box domain protein, chromatin remodelling complex protein, RNA methyltransferase. In summary, it appears that auxin responsive genes could be involved in regulating DOF and DFF as majority of the associated loci contained genes which are component of auxin signaling pathways in their vicinity. Overall, our findings indicates that the use of principal component analysis in GWAS is statistically more robust in terms of identifying genes and FarmCPU is a better choice compared to the other aforementioned models in dealing with both false positive and negative associations and thus can be used for traits with complex inheritance.

Bee Pollen and Probiotics May Alter Brain Neuropeptide Levels in a Rodent Model of Autism Spectrum Disorders.

Neuropeptides play a major role in maintaining normal brain development in children. Dysfunction of some specific neuropeptides can lead to autism spectrum disorders (ASD) in terms of social interaction and repetitive behavior, but the exact underlying etiological mechanisms are still not clear. In this study, we used an animal model of autism to investigate the role of bee pollen and probiotic in maintaining neuropeptide levels in the brain. We measured the Alpha-melanocyte-stimulating hormone (α-MSH), Beta-endorphin (β-End), neurotensin (NT), and substance P (SP) in brain homogenates of six studied groups of rats. Group I served as control, given only PBS for 30 days; Group II as an autistic model treated with 250 mg PPA/kg BW/day for 3 days after being given PBS for 27 days. Groups III-VI were denoted as intervention groups. G-III was treated with bee pollen (BP) 250 mg/kg body weight/day; G-IV with Lactobacillus paracaseii (LB) (109 CFU/mL) suspended in PBS; G-V with 0.2 g/kg body weight/day Protexin®, a mixture of probiotics (MPB); and G-VI was transplanted with stool from normal animals (FT) for 27 days prior to the induction of PPA neurotoxicity on the last 3 days of study (days 28–30). The obtained data were analyzed through the use of principal component analysis (PCA), discriminant analysis (DA), hierarchical clustering, and receiver operating characteristic (ROC) curves as excellent statistical tools in the field of biomarkers. The obtained data revealed that brain levels of the four measured neuropeptides were significantly reduced in PPA-treated animals compared to healthy control animals. Moreover, the findings demonstrate the ameliorative effects of bee pollen as a prebiotic and of the pure or mixed probiotics. This study proves the protective effects of pre and probiotics against the neurotoxic effects of PPA presented as impaired levels of α-MSH, β-End, NT, and SP.

Glycerol dialkyl glycerol tetraether signatures in tropical mesotidal estuary sediments of Qua Iboe River, Gulf of Guinea

The continuous supply of land derived organic carbon by rivers is significant to the ocean carbon cycle. In river-estuary systems, the composition and distribution of sedimentary organic matter (OM) can provide detailed information on the balance of land-derived inputs and in situ productivity. This insight is significant for an improved understanding of anthropogenic activity and the changing climate. In the Gulf of Guinea (GoG), high rainfall, coupled with flooding and a warmer climate have direct impacts on OM distributions. Glycerol dialkyl glycerol tetraethers (GDGTs) have been used extensively to investigate OM. In this study, we focused on the widespread occurrence of isoprenoid GDGTs (isoGDGTs), branched GDGTs (brGDGTs), H-tetraethers (H-isoGDGTs and H-brGDGTs), hydroxyl GDGTs (OH-GDGTs), isoGDGT isomers, methylated isoGDGTs (Me-GDGTs), butanetriol and pentanetriol tetraethers (BDGTs and PDGTs), in short sediment cores collected from the inner and outer Qua Iboe River Estuary, located in the GoG. We further determined total organic carbon (Corg), total nitrogen (TN), long chain hydrocarbons (LCA; C27–C33n-alkanes), dinosterol, brassicasterol and phytol, to estimate OM sources, flooding activity, OM degradation, to reconstruct pH and to determine past tropical temperatures. The distribution patterns of GDGTs, use of principal component analysis (PCA), heatmap hierarchical cluster analysis, correlation models, the ratios of isoGDGTs/brGDGTs, ring index (RI), brGDGT-IIIa/IIa, branched and isoprenoid tetraether (BIT), Corg/TN and terrestrial-marine biomarker ratio (TMBR) were all sensitive for indicating terrestrial vs marine OM contributions to the total OM of the tropical estuarine sediments. Marine OM persisted from subsurface to bottom sediment layers (8–38 cm) in the outer estuary site, while a mixed OM source was observed in inner estuary sediments. The Cren/Cren’ ratio >58.5 (up to 83.8), pointed to a predominance of Thaumarchaeota group I.1a microbes. The Methane Index (MI) and isoGDGT-0/Cren ratio indicated significant imprints of a non-methanotrophic archaeal community. A positive linearity of total BDGTs and PDGTs plotted against MI and isoGDGT-0/Cren suggested that these compounds are produced by the same group of the non-methanogenic archaeal community in this tropical estuary. Thus, Methanomassiliicoccus luminyensis is not the only source of BDGTs and PDGTs in the marine environment. Further, the calculated CBT5me derived pH reflected in situ measured sediment pH (6.3). TEX86H-derived sea surface temperature (SST), within error, reflected water column temperature. Calculated brGDGT mean annual air temperatures (MAATs) reflected average annual atmospheric temperatures. Ring index OH-GDGTs temperature proxies (RI-OH SSTsummer and RI-OHꞌ SST) reflected mean dry season (November to February) temperatures. A newly derived MAAT proxy, calculated from H-brGDGT, also reflected the tropical annual air temperature (23–28 °C). Altogether, as expected, the OM sources are traced largely to both terrestrial and marine inputs. The terrestrial OM was deposited by excess flooding and surface runoff occasioned by torrential monsoon rains in recent years, triggered by climate change, while mesotidal currents introduced marine OM into the Qua Iboe River Estuary system. This study further contributes to our understanding of OM distributions in warmer climate conditions.

In Silico Analysis of the Apoptotic and HPV Inhibitory Roles of Some Selected Phytochemicals Detected from the Rhizomes of Greater Cardamom.

Occurrence of cervical cancer, caused due to persistent human papilloma virus (HPV) infection, is common in women of developing countries. As the conventional treatments are expensive and associated with severe side effects, there is a need to find safer alternatives, which is affordable and less toxic to the healthy human cells. Present study aimed to evaluate the anti-HPV and apoptotic potential of four compounds from the greater cardamom (Amomum subulatum Roxb. var. Golsey), namely rhein, phytosphingosine, n-hexadecenoic acid and coronarin E. Their anti-HPV and apoptotic potential were studied against viral E6, E7 and few anti-apoptotic proteins of host cell (BCL2, XIAP, LIVIN) by in silico docking technique. Phytochemicals from the plant extract were analysed and identified by LC/MS and GC/MS. Involvement of the target proteins in various biological pathways was determined through KEGG. Structural optimization of the three-dimensional structures of the ligands (four phytochemicals and control drug) was done by Avogadro1.1. Receptor protein models were built using ProMod3 and other advanced tools. Pharmacophore modelling of the selected phytochemicals was performed in ZINCPharmer. Swiss ADME studies were undertaken to determine drug likeness. The ligands and proteins were digitally docked in DockThor docking program. Protein flexibility-molecular dynamic simulation helped to study protein–ligand stability in real time. Finally, the correlation of evaluated molecules was studied by the use of principal component analysis (PCA) based on the docking scores. All the ligands were found to possess apoptotic and anti-cancer activities and did not violate Lipinsky criteria. n-Hexadecanoic acid and its analogues showed maximum efficacy against the target proteins. All the protein–ligand interactions were found to be stable. The uncommon phytochemicals identified from rhizomes of greater cardamom have anti-cancer, apoptotic and HPV inhibitory potentials as analysed by docking and other in silico studies, which can be utilized in drug development after proper experimental validation.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12010-022-04006-3.