Profiling Techniques Research Articles

Optimized Trajectory Tracking for Robot Manipulators with Uncertain Dynamics: A Composite Position Predictive Control Approach

This study addresses the trajectory tracking control challenges of robot manipulators with uncertain dynamics. The aim is to achieve precise and smooth trajectory regulation through a novel composite position predictive control (PPC) scheme that integrates motion profile and disturbance preview techniques. First, we perform offline dynamics identification and feedforward compensation alongside a pre-defined motion profile. To handle the disturbances arising from uncertain dynamics, a super-twisting disturbance observer is designed, resulting in a dynamically compensated prediction model. Furthermore, the receding optimization operations for PPC are executed by solving an optimal solution associated with a joint angle tracking error. The combination of feedforward and feedback control improves the robot manipulator’s absolute positioning accuracy as opposed to the conventional model predictive control method, especially when dealing with uncertain dynamics. The effectiveness of the proposed control method is confirmed through trajectory tracking experiments conducted on a six-degree-of-freedom robot platform with varying end-effector loads. The experimental results demonstrate that the proposed PPC method enhances tracking accuracy by approximately 45% and 25% when compared to the traditional inverse dynamic control (IDC) and the robust IDC approaches, respectively.

Characteristics of the gut microbiome and metabolic profile in elderly patients with sarcopenia.

Introduction: There is growing evidence of research indicating that the gut microbiota is involved in the development of sarcopenia. Nevertheless, there exists a notable deficiency in comprehension concerning the connection between irregularities in the intestinal microbiome and metabolic processes in older individuals suffering from sarcopenia. Methods: To analyze fecal samples obtained from a cohort of 30 older patients diagnosed with sarcopenia as well as 30 older patients without sarcopenia, this study employed 16S rDNA sequencing and liquid chromatography-mass spectrometry (LC-MS)-based non-targeted metabolomics profiling techniques. Results: As a result, we found that 29 genera and 172 metabolites were significantly altered in the sarcopenic patients. Among them, Blautia, Lachnospiraceae_unclassified, and Subdoligranulum were the bacteria with a potential diagnostic value for sarcopenia diagnosis. Correlation analysis between clinical indices and these gut bacteria suggested that the IL-6 level was negatively correlated with Blautia. Function prediction analysis demonstrated that 17 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways differ significantly between sarcopenic and non-sarcopenic patients. The primary classes of metabolites identified in the study included lipids and lipid-like molecules, organic acids and derivatives, and organoheterocyclic compounds. KEGG enrichment analysis showed that purine metabolism, arginine and proline metabolism, alanine, aspartate, and glutamate metabolism, butanoate metabolism, and histidine metabolism may contribute to the development of sarcopenia. The correlation study on gut microbiota and metabolites found that Lachnospiraceae_unclassified was positively associated with seven metabolites that were more abundant in the non-sarcopenia group and negatively correlated with three metabolites that were more abundant in the sarcopenia group. In addition, Subdoligranulum was positively correlated with seven metabolites that were lacking in sarcopenia and negatively correlated with two metabolites that were enriching in sarcopenia. Moreover, Blautia was positively associated with xanthosine. Discussion: We conducted a study on the intestinal microbiota and metabolic profile of elderly individuals with sarcopenia, offering a comprehensive analysis of the overall ecosystem. Through this investigation, we were able to validate existing research on the gut-muscle axis and further investigate potential pathogenic processes and treatment options for sarcopenia.

Assessment the Subbottom Sedimentary Situation for Khor Abdullah, NW Arabian Gulf Using Sedimentary Coring Analysis and Sub-Bottom Profile Technique

The Coring Analysis and Sub-Bottom Profile technique are effective tools for assessing the sedimentary development of the bottoms, in this study these methods were used to assess the Sub bottom Sedimentary Situation for Khor Abdullah, NW Arabian Gulf. Two marine core sediments were studied at a depth of 40 m. Detailed grain-size analysis was carried out on 86 samples from cores. In addition, many sedimentary structures were diagnosed, such as load structures, wavy bedding, interfingered, peat layers, sole marks, and others. The environments and the transgression and regression of the sea were detected and interpreted on the basis of these analyzes, which were also attributed to the presence of the fauna in some beds.

Identification of Potential Genomic Alterations Using Pan-Cancer Cell-Free DNA Next-Generation Sequencing in Patients With Gastric Cancer.

Molecular cancer profiling may lead to appropriate trials for molecularly targeted therapies. Cell-free DNA (cfDNA) is a promising diagnostic and/or prognostic biomarker in gastric cancer (GC). We characterized somatic genomic alterations in cfDNA of patients with GC. Medical records and cfDNA data of 81 patients diagnosed as having GC were reviewed. Forty-nine and 32 patients were tested using the Oncomine Pan-Cancer Cell-Free Assay on the Ion Torrent platform and AlphaLiquid 100 kit on the Illumina platform, respectively. Tier I or II alterations were detected in 64.2% (52/81) of patients. Biomarkers for potential targeted therapy were detected in 55.6% of patients (45/81), and clinical trials are underway. ERBB2 amplification is actionable and was detected in 4.9% of patients (4/81). Among biomarkers showing potential for possible targeted therapy, TP53 mutation (38.3%, 35 variants in 31 patients, 31/81) and FGFR2 amplification (6.2%, 5/81) were detected the most. Next-generation sequencing of cfDNA is a promising technique for the molecular profiling of GC. Evidence suggests that cfDNA analysis can provide accurate and reliable information on somatic genomic alterations in patients with GC, potentially replacing tissue biopsy as a diagnostic and prognostic tool. Through cfDNA analysis for molecular profiling, it may be possible to translate the molecular classification into therapeutic targets and predictive biomarkers, leading to personalized treatment options for patients with GC in the future.

Tainted Love: a Systematic Literature Review of Online Romance Scam Research

Abstract Romance scams involve cybercriminals engineering a romantic relationship on online dating platforms for monetary gain. It is a cruel form of cybercrime whereby victims are left heartbroken, often facing financial ruin. We characterize the literary landscape on romance scams, advancing the understanding of researchers and practitioners by systematically reviewing and synthesizing contemporary qualitative and quantitative evidence. The systematic review establishes influencing factors of victimhood and explores countermeasures for mitigating romance scams. We searched 10 scholarly databases and websites using terms related to romance scams. The methodology followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines: a total of 279 papers were screened. One hundred seven papers were assessed for eligibility, and 53 were included in the final analysis. Three main contributions were identified: common profile features and techniques used by romance scammers, countermeasures for mitigating romance scams and factors predisposing an individual to become a scammer or a victim. Despite a growing corpus of literature, the total number of empirical or experimental examinations remained limited. The paper concludes with avenues for future research and victimhood intervention strategies for practitioners, law enforcement and the industry.

Postpartum defects in inflammatory response after gestational diabetes precede progression to type 2 diabetes: a nested case-control study within the SWIFT study

BackgroundGestational diabetes (GDM) is a distinctive form of diabetes that first presents in pregnancy. While most women return to normoglycemia after delivery, they are nearly ten times more likely to develop type 2 diabetes than women with uncomplicated pregnancies. Current prevention strategies remain limited due to our incomplete understanding of the early underpinnings of progression. AimTo comprehensively characterize the postpartum profiles of women shortly after a GDM pregnancy and identify key mechanisms responsible for the progression to overt type 2 diabetes using multi-dimensional approaches. MethodsWe conducted a nested case-control study of 200 women from the Study of Women, Infant Feeding and Type 2 Diabetes After GDM Pregnancy (SWIFT) to examine biochemical, proteomic, metabolomic, and lipidomic profiles at 6–9 weeks postpartum (baseline) after a GDM pregnancy. At baseline and annually up to two years, SWIFT administered research 2-hour 75-gram oral glucose tolerance tests. Women who developed incident type 2 diabetes within four years of delivery (incident case group, n = 100) were pair-matched by age, race, and pre-pregnancy body mass index to those who remained free of diabetes for at least 8 years (control group, n = 100). Correlation analyses were used to assess and integrate relationships across profiling platforms. ResultsAt baseline, all 200 women were free of diabetes. The case group was more likely to present with dysglycemia (e.g., impaired fasting glucose levels, glucose tolerance, or both). We also detected differences between groups across all omic platforms. Notably, protein profiles revealed an underlying inflammatory response with perturbations in protease inhibitors, coagulation components, extracellular matrix components, and lipoproteins, whereas metabolite and lipid profiles implicated disturbances in amino acids and triglycerides at individual and class levels with future progression. We identified significant correlations between profile features and fasting plasma insulin levels, but not with fasting glucose levels. Additionally, specific cross-omic relationships, particularly among proteins and lipids, were accentuated or activated in the case group but not the control group. ConclusionsOverall, we applied orthogonal, complementary profiling techniques to uncover an inflammatory response linked to elevated triglyceride levels shortly after a GDM pregnancy, which is more pronounced in women who progress to overt diabetes.

DNA barcodes, ISSR, RAPD and SCAR markers as potential quality control tools for molecular authentication of black and white mulberry

Morus species, especially black mulberry (Morus nigra L.), are well known for their nutritive and economic values. Black mulberry is often a subject for deliberate and undeliberate substitution with the morphologically similar white mulberry (Morus alba L.). In the current study, the genetic diversity of Morus nigra L. and Morus alba L. was assessed using different genetic profiling and fingerprinting techniques namely namely DNA barcoding, Random Amplified Polymorphic DNA (RAPD) and Inter Simple Sequence Repeat (ISSR). DNA barcoding was implemented using 2 plastid loci (matK, and rbcL-1) and 2 nuclear spacers (ITS and ITS2) aiming at accurate authentication of the studied species. DNA barcoding, targeting the selected loci succeeded to identify and authenticate the studies Morus species but failed to differentiate them, owing to their close genetic resemblance. Out of 52 screened RAPD primers, 27 were able to produce reproducible and clear profiles with 115 amplified fragments of which 37 were polymorphic accounting for 32.17% polymorphism. The number of amplified bands varied from 2 to 7 ranging in size from 100 to 700 bp. Moreover, 55 ISSR primers were evaluated, 108 fragments were produced, using 20 primers, of which 28 were polymorphic accounting for 25.92% polymorphism with size range of 200–800 bp. Sequence Characterized Amplified Region (SCAR) marker was developed based on reproducible banding pattern obtained by RAPD primer (OPG-03), for the purpose of accelerating the initial screening of Morus batches and enabling the preventative rejection of adulterated samples. The polymorphic band obtained was sequenced and specific primers were designed to develop a SCAR marker of approximately 558 bp exclusively for M. nigra, allowing detection of down to 1 ng, of M. nigra. The SCAR marker showed 100% success in differentiating authentic M. nigra and M. alba samples. In addition, the application of the developed SCAR marker was extended to distinguish M. nigra from its four common adulterants namely Ficus carica L., Fragaria vesca L., Ficus racemosa L. and Vitis vinifera L.. The proposed protocol was successfully implemented for the evaluation of 47 commercial M. nigra samples collected from different Egyptian locations.

Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation

Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.

Identification of variety-specific metabolites of basil by high performance thin layer chromatography-assisted metabolic profiling techniques

The technological advances of analytical instrumentation and techniques has laid the ground for the rapid expansion of metabolomics or in a wider sense, untargeted analysis applied to life sciences themes. However, the objective of identifying all existing metabolites within organisms remains a daunting challenge. All analytical techniques exhibit varying degrees of sensitivity and versatility for the detection of metabolites and none of the existing analytical platforms can be expected to be ideal for exhaustive chemical profiling. Planar liquid chromatography, and in particular, high performance thin layer chromatography (HPTLC), has been used for chemical profiling of natural products in conjunction with metabolomics. HPTLC has specific advantages which include its ability to generate reliable chemical fingerprinting data and facilitate preparative work for metabolite isolation during later stages of metabolomics analysis. In this study, we investigated the chemical profiles of four commercially available basil cultivars, namely Dolly, Emily, Keira, and Rosie. We used HPTLC as the primary analytical tool for the separation of basil cultivars based on detected metabolites, and then compared the results with those obtained with other analytical platforms. We identified the characteristic marker compounds of each basil cultivar from the HPTLC plates and validated their potential using LC-MS and GC-MS analyses as a metabolomics tool. Firstly, we compared the HPTLC data of the four cultivars, obtained with two systems that used silica gel 60 and two mobile phases composed of toluene-EtOAc (8:2, v/v) and EtOAc-formic acid-acetic acid-water (100:11:11:27, v/v), with 1H NMR data to evaluate their separation power. Despite providing lower resolution and detecting fewer compounds, the HPTLC separation power was comparable, and in some cases even better than that of 1H NMR. Additionally, we investigated the potential of HPTLC as a tool for chemical fingerprinting and demonstrated its suitability for preparative purposes that are essential for identifying metabolites in mixture analysis. Metabolites were easily isolated from sample mixtures, and identified with the assistance of GC-MS, LC-MS, and TLC-densitometry.. Several marker compounds were thus identified, including 2,4 di-tertbutylphenol, palmitic acid, hexadecanamide, 9-octadecenamide, squalene, hentriacontane, methyl 3-(3,5-ditert‑butyl‑4-hydroxyphenyl)propanoic acid, sagerinic acid, and cyanidin-3-O-sophoroside.

Neuroblastoma in the Era of Precision Medicine: A Clinical Review.

The latest advances in treatment for patients with neuroblastoma are constantly being incorporated into clinical trials and clinical practice standards, resulting in incremental improvements in the survival of patients over time. Survivors of high-risk neuroblastoma (HRNBL), however, continue to develop treatment-related late effects. Additionally, for the majority of the nearly 50% of patients with HRNBL who experience relapse, no curative therapy currently exists. As technologies in diagnostic and molecular profiling techniques rapidly advance, so does the discovery of potential treatment targets. Here, we discuss the current clinical landscape of therapies for neuroblastoma in the era of precision medicine.

Investigating pH Effects on Enzymes Catalyzing Polysorbate Degradation by Activity-Based Protein Profiling

Host cell proteins (HCPs) are process-related impurities that can negatively impact the quality of biotherapeutics. Some HCPs possess enzymatic activity and can affect the active pharmaceutical ingredient (API) or excipients such as polysorbates (PS). PSs are a class of non-ionic surfactants commonly used as excipients in biotherapeutics to enhance the stability of APIs. The enzyme activity of certain HCPs can result in the degradation of PSs, leading to particle formation and decreased shelf life of biotherapeutics. Identifying and characterizing these HCPs is therefore crucial. This study employed the Activity-Based Protein Profiling (ABPP) technique to investigate the effect of pH on the activity of HCPs that have the potential to degrade polysorbates. Two probes were utilized: the commercially available fluorophosphonate (FP)-Desthiobiotin probe and a probe based on the antiobesity drug, Orlistat. Over 50 HCPs were identified, showing a strong dependence on pH-milieu regarding their enzyme activity. These findings underscore the importance of accounting for pH variations in the ABPP method and other investigations of HCP activity. Notably, the Orlistat-based probe (OBP) enabled us to investigate the enzymatic activity of a wider range of HCPs, emphasizing the advantage of using more than one probe for ABPP. Finally, this study led to the discovery of previously unreported active enzymes, including three HCPs from the carboxylesterase enzyme family.

Gas Leakage Identification and Prevention by Pressure Profiling for Sustainable Supply of Natural Gas

In the strive for an environmentally conscious energy sector, a sustainable supply of natural gas necessitates comprehensive strategies that prioritise the prevention of gas leakages, ensuring minimal ecological harm while harnessing the benefits of this valuable resource. Though all sectors are putting their efforts into coping with the problems associated with the energy crisis, one of the many ways is to reduce distribution losses, particularly in the gas sector. The loss impacts include methane escaping to the environment to affect ozone, direct financial loss, non-availability of gas to the domestic, commercial, and industrial sectors, public safety hazards and indirect environmental impacts. There is a dire need to improve the natural gas distribution system, which has its own insight problems to be addressed. This work aims to investigate leakages in domestic, commercial and industrial gas distribution lines with different times, volumes and pressures using a pressure profiling technique. As a result of this investigation, around 293 million cubic feet (MMCF) of natural gas is saved, which is enough volume to feed approximately 16,000 domestic consumers for 365 days. The same energy content is equivalent to 21 million pounds of coal, which emits 64 million pounds of carbon dioxide, whereas natural gas would have 50% less CO2 emissions, and that is apart from the tangible pollution and waste associated with the use of coal.

Complete chloroplast genome of Lens lamottei reveals intraspecies variation among with Lens culinaris

Lens lamottei is a member of the Fabaceae family and the second gene pool of the genus Lens. The environmental factors that drove the divergence among wild and cultivated species have been studied extensively. Recent research has focused on genomic signatures associated with various phenotypes with the acceleration of next-generation techniques in molecular profiling. Therefore, in this study, we provide the complete sequence of the chloroplast genome sequence in the wild Lens species L. lamottei with a deep coverage of 713 × next-generation sequencing (NGS) data for the first time. Compared to the cultivated species, Lens culinaris, we identified synonymous, and nonsynonymous changes in the protein-coding regions of the genes ndhB, ndhF, ndhH, petA, rpoA, rpoC2, rps3, and ycf2 in L. lamottei. Phylogenetic analysis of chloroplast genomes of various plants under Leguminosae revealed that L. lamottei and L. culinaris are closest to one another than to other species. The complete chloroplast genome of L. lamottei also allowed us to reanalyze previously published transcriptomic data, which showed high levels of gene expression for ATP-synthase, rubisco, and photosystem genes. Overall, this study provides a deeper insight into the diversity of Lens species and the agricultural importance of these plants through their chloroplast genomes.

Effect of Citric Acid on the Properties of Sapwood of Pinus sylvestris Submitted to Thermomechanical Treatment

The present study aimed to evaluate the effect of citric acid on the properties of solid pine wood, which were submitted to thermomechanical treatment. A preliminary test was performed in a previous study to evaluate the influence of different temperatures of pressing and concentrations of citric acid on the physical properties of wood. After choosing the best treatments (170 °C and 5% and 10% of citric acid), the effect of these on the chemical properties (SEM, FTIR and pH); on the density profile using an X-ray microtomography and on the resistance to aging through an abrasion test were evaluated. The result of the chemical analysis showed an increase of the presence of ester functional groups, as well as better coverage and higher acidity of the surface. The density profile technique allowed us to observe the formation of peaks of density on the surface where the acid was applied. Lastly, it was also possible to verify an increase in the resistance to abrasions because of the application of citric acid.

Intracellular metabolic profiling of drug resistant cells by surface enhanced Raman scattering

BackgroundIntracellular metabolic profiling reveals real-time metabolic information useful for the study of underlying mechanisms of cells in particular conditions such as drug resistance. However, mass spectrometry (MS), one of the leading metabolomics technologies, usually requires a large number of cells and complex pretreatments. Surface enhanced Raman scattering (SERS) has an ultrahigh detection sensitivity and specificity, favorable for metabolomics analysis. However, some targeted SERS methods focus on very limited metabolite without global bioprofiling, and some label-free approaches try to fingerprint the metabolic response based on whole SERS spectral classification, but comprehensive interpretation of biological mechanisms was lacking. (95) ResultsWe proposed a label-free SERS technique for intracellular metabolic profiling in complex cellular lysates within 3 min. We first compared three kinds of cellular lysis methods and sonication lysis shows the highest extraction efficiency of metabolites. To obtain comprehensive metabolic information, we collected a spectral set for each sample and further qualified them by the Pearson correlation coefficient (PCC) to calculate how many spectra should be acquired at least to gain the adequate information from a statistical and global view. In addition, according to our measurements with 10 pure metabolites, we can understand the spectra acquired from complex cellular lysates of different cell lines more precisely. Finally, we further disclosed the variations of 22 SERS bands in enzalutamide-resistant prostate cancer cells and some are associated with the androgen receptor signaling activity and the methionine salvage pathway in the drug resistance process, which shows the same metabolic trends as MS. (149) SignificanceOur technique has the capability to capture the intracellular metabolic fingerprinting with the optimized lysis approach and spectral set collection, showing high potential in rapid, sensitive and global metabolic profiling in complex biosamples and clinical liquid biopsy. This gives a new perspective to the study of SERS in insightful understanding of relevant biological mechanisms. (54).

Multi-omics regulatory network inference in the presence of missing data.

A key problem in systems biology is the discovery of regulatory mechanisms that drive phenotypic behaviour of complex biological systems in the form of multi-level networks. Modern multi-omics profiling techniques probe these fundamental regulatory networks but are often hampered by experimental restrictions leading to missing data or partially measured omics types for subsets of individuals due to cost restrictions. In such scenarios, in which missing data is present, classical computational approaches to infer regulatory networks are limited. In recent years, approaches have been proposed to infer sparse regression models in the presence of missing information. Nevertheless, these methods have not been adopted for regulatory network inference yet. In this study, we integrated regression-based methods that can handle missingness into KiMONo, a Knowledge guided Multi-Omics Network inference approach, and benchmarked their performance on commonly encountered missing data scenarios in single- and multi-omics studies. Overall, two-step approaches that explicitly handle missingness performed best for a wide range of random- and block-missingness scenarios on imbalanced omics-layers dimensions, while methods implicitly handling missingness performed best on balanced omics-layers dimensions. Our results show that robust multi-omics network inference in the presence of missing data with KiMONo is feasible and thus allows users to leverage available multi-omics data to its full extent.

Hydrogen Exchange and Diffusion Kinetics at Elevated Temperatures in Proton-Conducting Solid Oxide Materials

Storage of purified hydrogen is one of the central challenges in addressing climate change and reducing our reliance on fossil fuels for energy conversion and storage, and therefore there is a global surge in research and development concerning hydrogen purification and storage. In this regard, we are studying proton conduction in solid oxide materials at elevated temperatures for applications in hydrogen separation and compression membranes. Hydrogen compression is the most recommended method to store hydrogen for automotive applications as it allows an increase in the hydrogen volumetric energy density.Traditionally the protonic conductivity in these materials is measured by indirect methods. For example, conductivity measurements in mixed gas atmospheres, comparing for example dry N2 with humidified N2, thereby allowing the contribution of protons to be evaluated. In this study, we for the first time report the evaluation of protonic conductivity in BaZr1-xCexY0.2O3−δ, BaZr0.1Ce0.7Y0.2–xYbxO3–δ and Ba7Nb4MoO20 by direct measurements afforded by the Isotope Exchange Depth Profiling technique with deuterium labelling. We also report the kinetics of H/D transport through the bulk materials and across metal-ceramic interfaces with a particular interest in the behaviour of the interface between the key Pd/Pd alloy catalyst component and the hydrogen-transporting oxide ceramic material. The transport and interface behaviour information will be of significance in designing hydrogen separation and compression membranes. Figure 1

Risk-Stratified Therapy for Pediatric Acute Myeloid Leukemia.

Acute Myeloid Leukemia (AML) is the second most common type of leukemia in children. Recent advances in high-resolution genomic profiling techniques have uncovered the mutational landscape of pediatric AML as distinct from adult AML. Overall survival rates of children with AML have dramatically improved in the past 40 years, currently reaching 70% to 80% in developed countries. This was accomplished by the intensification of conventional chemotherapy, improvement in risk stratification using leukemia-specific cytogenetics/molecular genetics and measurable residual disease, appropriate use of allogeneic hematopoietic stem cell transplantation, and improvement in supportive care. However, the principle therapeutic approach for pediatric AML has not changed substantially for decades and improvement in event-free survival is rather modest. Further refinements in risk stratification and the introduction of emerging novel therapies to contemporary therapy, through international collaboration, would be key solutions for further improvements in outcomes.

Advances of hyphenated technique in impurity profiling of active pharmaceutical ingredients and pharmaceutical products

AbstractImpurities found in active pharmaceutical ingredients (APIs) and pharmaceutical products are of ever‐increasing interest. According to several regulatory agencies, purity and impurity profiles are essential. An impurity is defined as any additional inorganic or organic material, residual solvents other than the medicinal components, or undesired compounds that remain with APIs. Impurities and degradation products in bulk drug materials and pharmaceutical formulations are identified, their structures are clarified, and their quantitative determination is part of impurity profiling. Unrecognized, poisonous impurities are dangerous to health and should be identified by selective procedures to increase the safety of drug therapy, and impurity profiling has become more significant in pharmaceutical analysis. This review briefly introduces process and product‐related impurities and emphasizes the creation of cutting‐edge analytical techniques for identifying them. It discusses the use of analytical methods, particularly high‐performance thin‐layer chromatography, liquid chromatography with mass spectrometry (MS), ultrahigh‐performance liquid chromatography, gas chromatography–MS, and nuclear magnetic resonance spectroscopy for the identification of contaminants and degradation products. It has discussed the importance of the quality, efficacy, and safety of drug substances and products, including the origin, types, and quality control of impurities, the need for the development of impurity profiling methods, impurity identification, and regulatory aspects.

Load profiling of commercial and residential building using clustering technique

Data mining is a promising tool used in processing energy data collected from energy consumers. The knowledge derived from energy data is very pertinent in the formulation of various demand-side management programs. This paper uses clustering techniques to segment the energy consumption patterns of residential and commercial buildings; situated at different geographical locations. The two (2) commonly used clustering techniques: K-Means and Agglomerative Hierarchical Clustering, were employed. The result indicates that the choice of clustering technique for load profiling is highly subjective to the nature of the dataset. Hence, using Davies-Bouldin Index (DB) Index and Silhouette Index (SI) as clustering indicators to select an optimum number of clusters and the best clustering technique. Hierarchical clustering was identified as the most appropriate clustering for the two buildings.