Analysis Approach Research Articles

Exploring cocrystals and polymorphism in pharmaceutical science: A comprehensive review

The pharmaceutical industry has been working hard in recent years to find ways to control polymorphism, which involves many solid drug forms due to their various physical and chemical characteristics. Creating cocrystals and salts of active pharmaceutical ingredients (APIs) is one potential research direction. These substances have drawn a lot of attention because they can improve crucial pharmacological features without reducing therapeutic efficacy. It is significant to note that not all compounds having multiple hydrogen bond donor/acceptor groups and polymorphism properties will form cocrystals; this behavior varies. Pharmaceutical firms and researchers alter APIs, frequently by generating cocrystals, salts, or polymorphs. This review discusses analytical approaches for solids drug forms such as diffraction, spectroscopy, thermal analysis, and pharmaceutical characterization. These techniques enable us to investigate solid APIs thoroughly, from their bulk characteristics to their molecular composition. They offer vital details regarding the composition, attributes, purity, and probable changes of the substance. These technologies are also useful for continuous monitoring and analysis of drug development physical procedures.

Women’s memory of episiotomy experience from 2 months to 2 years after birth in Orlu Imo State

Episiotomy procedures are defined based on the millimetre distance from the incision point to the posterior fourchette and the angle in degrees from the sagittal or parasagittal plane. This study examined Women's Memory of Episiotomy Experience from 2 Months to 2 Years after Birth in Orlu of Imo State. The study employed a qualitative survey design with four research questions. The sample size of 57 participants was selected, convenience sampling technique was used to select the respondents, the respondent were thus; women who underwent episiotomy 25, Midwives 8, family members 24. Data were collected using one on-one interview, via recordings. Data was analysed using percentages, barcharts and content analysis approach and thematic categorization. The results revealed 60% of the respondents come from low socioeconomic background. The study further revealed that; the challenges women experience within 2 months to 2 years as result of episiotomy were pain, inability to breast feed; due to inability to sit well, inability to walk freely, the perceived impact of episiotomy on women within the first 2 months to 2 years after birth were inability to have sex as usual. In conclusion, women were inadequately informed and not psychologically prepared for episiotomy, the respondent’s experienced untoward effect after the procedure which included: pain, and interference with daily life activities. Therefore, the study recommends that healthcare providers, especially midwives, to seek alternatives to routine use of episiotomy, and consider selective episiotomy practices based on individual patient need and thus educate pregnant mothers on perineal massage using recommended oil.

Uncover Spatially Informed Variations for Single-Cell Spatial Transcriptomics with STew

Abstract Motivation The recent spatial transcriptomics (ST) technologies have enabled characterization of gene expression patterns and spatial information, advancing our understanding of cell lineages within diseased tissues. Several analytical approaches have been proposed for ST data, but effectively utilizing spatial information to unveil the shared variation with gene expression remains a challenge. Results We introduce STew, a Spatial Transcriptomic multi-viEW representation learning method, to jointly analyze spatial information and gene expression in a scalable manner, followed by a data-driven statistical framework to measure the goodness of model fit. Through benchmarking using human dorsolateral prefrontal cortex (DLPFC) and mouse main olfactory bulb (MOB) data with true manual annotations, STew achieved superior performance in both clustering accuracy and continuity of identified spatial domains compared with other methods. STew is also robust to generate consistent results insensitive to model parameters, including sparsity constraints. We next applied STew to various ST data acquired from 10x Visium, Slide-seqV2, and 10x Xenium, encompassing single-cell and multi-cellular resolution ST technologies, which revealed spatially informed cell type clusters and biologically meaningful axes. In particular, we identified a pro-inflammatory fibroblast spatial niche using ST data from psoriatic skins. Moreover, STew scales almost linearly with the number of spatial locations, guaranteeing its applicability to datasets with thousands of spatial locations to capture disease relevant niches in complex tissues. Availability Source code and the R software tool STew are available from github.com/fanzhanglab/STew. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Hybrid deep learning approach for sentiment analysis using text and emojis.

Sentiment Analysis (SA) is a technique for categorizing texts based on the sentimental polarity of people's opinions. This paper introduces a sentiment analysis (SA) model with text and emojis. The two preprocessed data's are data with text and emojis and text without emojis. Feature extraction consists text features and text with emojis features. The text features are features like N-grams, modified Term Frequency-Inverse Document Frequency (TF-IDF), and Bag-of-Words (BoW) features extracted from the text. In classification, CNN (Conventional Neural Network) and MLP (Multi-Layer Perception) use emojis and text-based SA. The CNN weight is optimized by a new Electric fish Customized Shark Smell Optimization (ECSSO) Algorithm. Similarly, the text-based SA is carried out by hybrid Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) classifiers. The bagged data are given as input to the classification process via RNN and LSTM. Here, the weight of LSTM is optimized by the suggested ECSSO algorithm. Then, the mean of LSTM and RNN determines the final output. The specificity of the developed scheme is 29.01%, 42.75%, 23.88%,22.07%, 25.31%, 18.42%, 5.68%, 10.34%, 6.20%, 6.64%, and 6.84% better for 70% than other models. The efficiency of the proposed scheme is computed and evaluated.

Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, and health risk model.

An in-depth understanding of nitrate-contaminated surface water and groundwater quality and associated risks is important for groundwater management. Hydrochemical characteristics and driving forces of groundwater quality and non-carcinogenic risks of nitrate were revealed by the integrated approaches of self-organizing map analysis, spatial visualization by geography information system, entropy and irrigation water quality indices, and human health risk model. Groundwater samples were categorized into two clusters by SOM analysis. Cluster I including three samples were Ca-SO4 type and cluster II of remaining 136 samples were Ca-HCO3 type. Hydrochemical compositions of two cluster samples were dominated by water-rock interaction: (1) calcite and gypsum dissolution for cluster I samples and (2) calcite dissolution, silicate weathering, and positive cation exchange for cluster II samples. Nitrate contamination occurred in both cluster I and II samples, primarily induced by agricultural nitrogen fertilizer. The EWQI results showed that 90.97% in total groundwater samples were suitable for drinking purpose, while the IWQI results demonstrated that 65.03% in total groundwater samples were appropriate for irrigation purpose. The HHR model and Monte Carlo simulation indicated that the non-carcinogenic nitrated risk was highest in children. Exposure frequency was the most sensitive factor (86.33% in total) influencing the total non-carcinogenic risk, indicated by sensitivity analysis. Compared with the two clusters of groundwater, surface water has a shorter circulation cycle and lower ion concentrations resulting in better water quality. This study can provide scientific basis for groundwater quality evaluation in other parts of the world.

Biochemical properties of chromatin domains define genome compartmentalization.

Chromatin three-dimensional (3D) organization inside the cell nucleus determines the separation of euchromatin and heterochromatin domains. Their segregation results in the definition of active and inactive chromatin compartments, whereby the local concentration of associated proteins, RNA and DNA results in the formation of distinct subnuclear structures. Thus, chromatin domains spatially confined in a specific 3D nuclear compartment are expected to share similar epigenetic features and biochemical properties, in terms of accessibility and solubility. Based on this rationale, we developed the 4f-SAMMY-seq to map euchromatin and heterochromatin based on their accessibility and solubility, starting from as little as 10000 cells. Adopting a tailored bioinformatic data analysis approach we reconstruct also their 3D segregation in active and inactive chromatin compartments and sub-compartments, thus recapitulating the characteristic properties of distinct chromatin states. A key novelty of the new method is the capability to map both the linear segmentation of open and closed chromatin domains, as well as their compartmentalization in one single experiment.

Self-managing peer team as a facilitator of online teaching

PurposeThis qualitative study assesses the enablers of the work of a novel, self-managing digital pedagogy peer support team in a Finnish higher education institution.Design/methodology/approachThis study employs a qualitative methodology with in-depth interviews of five digital pedagogy facilitators. The data collected are analyzed with the ATLAS.ti software. The analytical approach follows a deductive method, applying the categories derived from Magpili and Pazos (2018) who investigated the input variables of self-managing teams through their extensive literature review.FindingsThe primary findings underscore the suitability of Magpili and Pazos' variables for evaluating the performance enablers of self-managing teams. Furthermore, the findings emphasize the significance of leadership and effective communication as essential prerequisites for achieving elevated performance levels.Research limitations/implicationsThis study focuses on the enablers of team performance from the perspective of the team members. To enhance comprehensiveness, subsequent phases should incorporate viewpoints from clients, namely peer instructors, and focus on the mediator and outcome aspects of the team effectiveness framework.Practical implicationsThis study offers actionable recommendations for higher education institutions aiming to adopt a peer mentor model akin to the one delineated in the study.Originality/valueThis study analyzes a collaborative approach to advancing digital pedagogy within higher education institutions and discusses the enablers for successful performance within self-managing teams.

Determinants of physical activity promotion in primary care from the patient perspective of people at risk of or living with chronic disease: a COM-B analysis

BackgroundChronic disease (CD) accounts for more than half of the overall global disease burden and physical activity (PA) is an established evidence-based strategy for the prevention and management of CD. Global policy emphasises the value of embedding PA into primary healthcare, highlighting the positive effects on PA behaviour. However, there is limited implementation of PA protocols in primary care, and research is needed to guide its integration into routine practice. The voice of the patient is underrepresented in the literature, resulting in the absence of critical insights into determinants of PA promotion in primary care. The purpose of the research was to identify the perspectives of people at risk of or living with CD on the determinants of PA promotion in primary care and to map these determinants across the six COM-B constructs.MethodsSemi-structured interviews (n = 22), guided by the COM-B model were conducted with people aged 35–60 years, at risk of or living with CD and not meeting the PA guidelines. A hybrid analytic approach of thematic inductive and deductive analysis was applied to the participant transcripts guided by a COM-B informed coding framework.ResultsIn total, 37 determinants across constructs related to capability, opportunity and motivation were prominent, examples include; physical capability constraints, the conflation of exercise with weight management, credibility of the health services in PA advice, communication styles in PA promotion, expectations of tailored support for PA, social support, accessibility, and integration of PA into routine habits.ConclusionExploring the determinants of PA promotion through the lens of the COM-B model facilitated a systematic approach to understanding the primary care user perspective of the healthcare professional (HCP) broaching the topic of PA. Findings emphasise the value of HCPs being supported to broach the issue of PA in a therapeutic and patient-centred manner using diverse and flexible approaches, while highlighting the importance of tailored, accessible PA opportunities that build self-efficacy and foster social support. The research provides valuable learnings to support PA promotion and the development of strategies in primary care through encompassing the perspective of those living with or at risk of CD.

Transient forward harmonic adjoint sensitivity analysis

AbstractThis paper presents a transient forward harmonic adjoint sensitivity analysis (TFHA), which is a combination of a transient forward circuit analysis with a harmonic balance-based adjoint sensitivity analysis. TFHA provides sensitivities of quantities of interest from time-periodic problems with many design parameters, as used in the design process of power-electronics devices. The TFHA shows advantages in applications where the harmonic balance-based adjoint sensitivity analysis or finite difference approaches for sensitivity analysis perform poorly. In contrast to existing methods, the TFHA can be used in combination with arbitrary forward solvers, i.e., general transient solvers.

ITER: an ITERative approach for inter-core timing analysis in statically scheduled cyclic executive systems on COTS multicore platforms for CRTES

ITER: an ITERative approach for inter-core timing analysis in statically scheduled cyclic executive systems on COTS multicore platforms for CRTES

Scientific text citation analysis using CNN features and ensemble learning model.

Citation illustrates the link between citing and cited documents. Different aspects of achievements like the journal's impact factor, author's ranking, and peers' judgment are analyzed using citations. However, citations are given the same weight for determining these important metrics. However academics contend that not all citations can ever have equal weight. Predominantly, such rankings are based on quantitative measures and the qualitative aspect is completely ignored. For a fair evaluation, qualitative evaluation of citations is needed in addition to quantitative ones. Many existing works that use qualitative evaluation consider binary class and categorize citations as important or unimportant. This study considers multi-class tasks for citation sentiments on imbalanced data and presents a novel framework for sentiment analysis in in-text citations of research articles. In the proposed technique, features are retrieved using a convolutional neural network (CNN), and classification is performed using a voting classifier that combines Logistic Regression (LR) and Stochastic Gradient Descent (SGD). The class imbalance problem is handled by the synthetic minority oversampling technique (SMOTE). Extensive experiments are performed in comparison with the proposed approach using SMOTE-generated data and machine learning models by term frequency (TF), and term frequency-inverse document frequency (TF-IDF) to evaluate the efficacy of the proposed approach for citation analysis. It is found that the proposed voting classifier using CNN features achieves an accuracy, precision, recall, and F1 score of 0.99 for all. This work not only advances the field of sentiment analysis in academic citations but also underscores the importance of incorporating qualitative aspects in evaluating the impact and sentiments conveyed through citations.

Is science to be trusted? How environmentally active youths relate to science in social media.

Research has shown a great distrust among youths toward political representatives, who they demand should "listen to the science." However, less research has been done on youths' own trust in science. This study explores and explains how youths who are environmentally active in two different environmental youth organizations relate to science in social media, whether they trust science, and how youths' relation to science creates a discursive context in which they may develop their identity. The study uses the approach of discourse analysis to examine social media content published on Facebook by Fridays for Future Sweden and Fältbiologerna (the Swedish Field Biologists). The study shows (i) how subject positions for scientists and youth are created in relation to one another based on different expressions of youths' trust in science and (ii) how environmental youth organizations, by identifying with science, make youths important actors in the discourse on climate change.

Experimental and numerical investigations of the water surface profile and wave extrema of supercritical flows in a narrow channel bend

Supercritical flows in channel bends, e.g., in steep streams, chute spillways, and flood and sediment bypass tunnels (SBTs), experience cross-waves, which undulate the free surface. The designs of these hydraulic structures and flood protection retaining structures in streams necessitate computing the locations and water depths of the wave extrema. This study numerically and experimentally investigates the water surface profiles along the sidewalls, the wave extrema flow depths, and their angular locations in a narrow channel bend model of the Solis SBT in Switzerland. The 0.2 m wide and 16.75 m long channel has a bend of 6.59 m radius and 46.5° angle of deviation. The tested flow conditions produced Froude numbers ≈ 2 and aspect ratios ranging from 1.14 to 1.83. Two-phase flow simulations were performed in OpenFOAM using the RNG k–ε turbulence closure model and the volume-of-fluid method. The simulated angular locations of the first wave extrema and the corresponding flow depths deviate marginally, within ± 6.3% and ± 2.1%, respectively, from the experimental observations, which signifies good predictions using the numerical model. Larger deviations, especially for the angular locations of the wave extrema, are observed for the existing analytical and empirical approaches. Therefore, the presented numerical approach is a suitable tool in designing the height of the hydraulic structures with bends and conveying supercritical flows. In the future, the model’s application shall be extended to the design of the height and location of retaining walls, embankments, and levees in steep natural streams with bends.

Comparisons of Different Representative Species Selection Schemes for Reduced-Order Modeling and Chemistry Acceleration of Complex Hydrocarbon Fuels

The simulation of engine combustion processes, such as autoignition, an important process in the co-optimization of fuel-engine design, can be computationally expensive due to the large number of thermo-chemical scalars needed to describe the full chemical system. Yet, the inherent correlations between the different chemical species during oxidation can significantly reduce the complexity of representing this system. One strategy is to select a subset of representative species that accurately captures the combustion process at a fraction of the computational cost of the full system. In this study, we compare the performance of four different techniques to select these species. They include the two-step principal component analysis (PCA) approach, directed relation graphs (DRGs), the global pathway selection (GPS) approach, and the manifold-informed species selection method. A parametric study of the representative species selection is carried out on data from the simulation of homogeneous and perfectly stirred reactors by investigating seven cumulative variances and 47 different cut-off percentages for the two-step PCA, and 65 and 51 thresholds for the DRGs and GPS, respectively. Results show that these selection methods capture key important species that can accurately describe the chemical system and track each stage of oxidation. The two-step PCA is sensitive to the cumulative variance, and DRGs and GPS are sensitive to the choice of target variables. By selecting key representative species and reducing the number of thermo-chemical scalars, these three methods can be used to develop computationally efficient hybrid chemistry schemes.

Enhancing interpretability in neural networks for nuclear power plant fault diagnosis: A comprehensive analysis and improvement approach

Deep neural networks, as applied in the field of nuclear power fault diagnosis, have garnered significant attention alongside advancements in artificial intelligence technology. However, the “black box” nature of deep learning models has raised concerns regarding their deployment in scenarios demanding high safety standards, such as nuclear power plants. In this paper, we innovatively propose the utilization of an explainable artificial intelligence method, grounded in game theory, to conduct a detailed analysis of the diagnostic behavior of neural network models applied to nuclear power plants. By leveraging SHAP, the decision-making processes of these opaque models are demystified, offering insights into how and why they arrive at their predictions. In this study, two of the most widely utilized neural network frameworks are applied across six representative fault diagnosis cases for a comprehensive analysis. Based on its analysis results, a novel SHAP-Enhanced Feature Selection for Efficient Neural Network Fault Diagnosis strategy is proposed, significantly reducing model complexity without sacrificing diagnostic performance. This research breaks through the limitations of data-driven models used in the field of nuclear power fault diagnosis, conducts an interpretable analysis of their behavior, and proposes an improved strategy based on the analysis results, contributing to the practical application of data-driven models in nuclear power.

Psychological Characteristics of Students with Passion for Studying

Passion for studying can be considered a significant factor that promotes well-being and mental health in students. This study aimed to examine whether the psychological characteristics of students with a passion for studying differed from those of students without one. To compare these two groups, we used a set of different psychological variables (e.g., academic burnout and vitality), as well as integrated both person-centered (i.e., group comparison research) and variable-centered (i.e., correlational analysis) approaches. During classes, one hundred and fifty-four students from a Polish university completed a comprehensive set of short self-report questionnaires online on different psychological characteristics, including variables related to studying (i.e., passion for studying, academic burnout, and general academic self-efficacy), psychopathology symptoms, perceived stress and somatic complaints, as well as personal resources (vitality, resilience, self-esteem, and optimism). We noted multiple statistically significant differences in psychological characteristics between the two studied groups of students. Thus, harmonious passionate students tended to have more favorable psychological characteristics within variables related to studying, mental or somatic health symptoms, and personal resources compared to the non-passionate students. A harmonious passion for studying seems to have potential health-promoting and health-protecting effects, whereas a lack of passion for studying may lead to less favorable outcomes.

Isolation and identification of hyperin and naringenin from guava cultivated in Iraq and evaluation of cytotoxic activity of hyperin

The guava plant, Psidium guajava L., serves as proof of the abundant donations of nature, providing a delicious guava fruit; this plant is rich in groups of medicinal and nutritional benefits. Guava belonging to the Myrtaceae family, many previous studies reported many phytochemical constituents in its leaves that have many pharmacological activities and medicinal properties; this study focuses on the isolation, structural elucidation and calculation concentration of flavonoids, assessment of the cytotoxic activityof hyperin from Psidium guajava leaves newly cultivated in Iraq. The isolation process involved the use of thin-layer chromatography (TLC) and preparative high-performance liquid chromatography (PHPLC) and structural elucidation involved NMR (nuclear magnetic resonance spectroscopy) and FTIR (Fourier transform infrared spectrometer) for detailed structural insights into 2 isolated flavonoids. The isolation techniques proved effective in obtaining pure samples of hyperin and naringenin from the ethyl acetate and n.butanol fractions and structural elucidation techniques gives a good explanation for 2 isolated flavonoids. Evaluate cytotoxic activity of hyperin flavonoid against prostate cancer cell line (PC-3), human breast cancer cell line (MCF-7) and normal dermal fibroblast neonatal (HdFn), hyperin flavonoid exhibited a decrease in cell viability (%) with IC50 58.9 ?g /mL against the prostate cancer cell and IC50 90.58 ?g /mL against human breast cancer and noncytotoxic to HDFn, with an IC50 value substantially surpassing concentrations of 100 ?g/mL. These analytical approaches provided a comprehensive understanding of the chemical composition of the isolated flavonoids and interpreted that guava has cytotoxic activity against some cancers, depending on a concentration-dependent mode.

Analytical solution for single-pixel ptychography through linear modeling

Amplitude-modulated single-pixel ptychography (SPP) enables non-interferometric complex-field imaging of objects. However, the conventional iterative and nondeterministic reconstruction methods, based on the ptychography algorithm, pose challenges in fully understanding the role of critical optical parameters. In response, this paper introduces an innovative analytical approach that establishes a theoretical foundation for the uniqueness of SPP reconstruction results. The proposed method conceptualizes SPP as a system of linear equations in the frequency domain, involving both object and modulated illumination. Solving this equation system reveals a determined solution for the complex object, providing an alternative to iterative and nondeterministic techniques. Through a series of simulations, this approach not only validates the uniqueness of SPP reconstruction, but also explores key properties influencing accuracy.

Numerical simulations and analytical approach for three-component coupled NLS-type equations in fiber optics

Numerical simulations and analytical approach for three-component coupled NLS-type equations in fiber optics

A novel analytical approach to vertical dynamic vibration of pipe‐pile groups considering soil‐plug effect

AbstractA novel pile‐to‐pile interaction model for pipe piles that considers the soil‐plug effect is established based on the Novak plane‐strain soil model. Subsequently, the pipe‐pile dynamic interaction factor and dynamic impedance of pipe‐pile groups are derived using the superposition method. The accuracy of the proposed analytical solution is confirmed by comparison with experimental results and existing solutions. Finally, numerical results are obtained to study the influence of the soil‐plug height, shear modulus of the soil, pile spacing, and pile outer radius on the pipe‐pile dynamic interaction factor and dynamic impedance of pipe‐pile groups. The results indicate that the effect of the soil‐plug height on the pipe‐pile dynamic interaction factor and dynamic impedance is significant, whereas the shear modulus of the soil plug has virtually no effect. The proposed analytical solution can be applied to determine the optimal values of pile and soil parameters during the dynamic design of pipe‐pile groups.