Step In Analysis Research Articles

AFSC: A self-supervised augmentation-free spatial clustering method based on contrastive learning for identifying spatial domains

Recent research in spatial transcriptomics allows researchers to analyze gene expression without losing spatial information. Spatial information can assist in cell communication, identification of new cell subtypes, which provides important research methods for multiple fields such as microenvironment interactions and pathological processes of diseases. Identifying spatial domains is an important step in spatial transcriptomics analysis, and improving spatial clustering methods can benefit for identifying spatial domains. In addition to eliminating noise in original gene expression, how to use spatial information to assist clustering has also become a new problem. A variety of calculating methods have been applied to spatial clustering, including contrastive learning methods. However, existing spatial clustering methods based on contrastive learning use data augmentation to generate positive and negative pairs, which will inevitably destroy the biological meaning of the data. We propose a new self-supervised spatial clustering method based on contrastive learning, Augmentation-Free Spatial Clustering (AFSC), which integrates spatial information and gene expression to learn latent representations. We construct a contrastive learning module without negative pairs or data augmentation by designing Teacher and Student Encoder. We also design an unsupervised clustering module to make clustering and contrastive learning be trained together. Experiments on multiple spatial transcriptomics datasets at different resolutions demonstrate that our method performs well in self-supervised spatial clustering tasks. Furthermore, the learned representations can be used for various downstream tasks including visualization and trajectory inference.

The Time Is Ripe: Olive Drupe Maturation Can Be Simply Evidenced by a Miniaturized, Portable and Easy-to-Use MicroNIR Green Sensor

The analytical study described in this work, based on NIR spectroscopy with a handheld device, allowed the development of a chemometric prediction model that has been validated for the objective evaluation of the ripening of olive drupes. The miniaturized, portable NIR spectrometer is proposed here as an easy-to-use sensor able to estimate the best harvesting time for ripening of olive drupes. The MicroNIR/chemometrics approach was developed for on-site identification of olive drupe ripening directly on plants, avoiding collection and successive laboratory analysis steps. A supporting parallel characterization by chromatographic techniques validated the spectroscopic prediction. The novelty of this approach consists in the possibility of investigating the olive drupe maturation point by collecting spectra in the near-infrared region and processing them using a chemometric model. The fast and accurate device allows one to easily follow the spectrum profile changes of olive drupes during ripening, thus preserving the fruits from being harvested too early or too late. The results of this study demonstrate the possibility of using the MicroNIR/chemometrics approach to determine the optimal ripening time of olives regardless of the plant variety, age and cultivation location. The results consequently demonstrated that the MicroNIR/chemometrics approach can be proposed as a new method to perform on-site evaluation of ripening by a single-click device. It can be conveniently used by any operator, who does not necessarily have to be expert but must simply be trained to use spectroscopy and a prediction model.

Finite element analysis of bolted joints under torsional loads

The presence of service torque, which is generated after tightening, directly affects the mechanical properties of bolted joint. In this work, the mechanical behavior of bolted joints under torsional loads is investigated using finite element analysis (FEA). Models with two plates and multiple plates are established separately to investigate the mechanical properties of different kinds of bolted joints. Three analysis steps involving the entire process from tightening to torsional loading are adopted, namely tightening step, initial loss step and loading step. Torsional loads in two directions along the thread axis are considered and defined as loosening torsional load and tightening torsional load. The clamping force, as well as the friction torques of various contact surfaces, of the bolted joint are extracted in all the analysis steps. Different scenarios of friction torque combinations for bolted joints are obtained by means of adjusting the friction coefficient of the corresponding contact surfaces. On this basis, the mechanical properties of bolted joints and the corresponding torque thresholds are extracted on various scenarios. FEA results showed that how the service torque makes the directional characteristic of the mechanical properties of bolted joints under torsional loads. Moreover, the service reliability of bolted joint can be further improved based on the directional characteristics and the extracted torque thresholds.

Pesticides and transformation products footprint in Greek market basket vegetables: Comprehensive screening by HRMS and health risk assessment

Healthy dietary habits encourage vegetable consumption. Although pesticide use in crops may negatively affect human health through food intake, it can also contaminate aquatic and terrestrial environments. Thus, monitoring pesticides in high-consumption matrices is crucial. This study conducted a complete workflow of analysis, including a step of target analysis of 30 widely used pesticides and a subsequent step of suspect screening. A validated QuEChERS method was employed to analyze 61 samples of fruiting vegetables and cucurbits, packaged leafy greens, and root and tuber vegetables, commercially distributed across Greece. The method proved to be highly efficient for all validation characteristics. After target analysis, the change in the residue levels detected during sample processing was evaluated as a case study using available literature data. A health risk assessment based on diet indicated acute and chronic hazard quotients (aHQ and cHQ) and chronic hazard index (cHI) values below 1. Concerning suspect screening, 53 additional identifications of pesticides and transformation products (TPs) were revealed, totaling 86 detections. Overall, 18 parent pesticide compounds and 5 TPs were identified. Ultimately, this approach is expected to provide added value in pesticide and TPs analysis of food matrices without prior data, minimizing experimental time and costs.

Microwave digestion method for sulfide analysis of Çanakkale region coal

ABSTRACT Quantitative analysis of sulfide content in coal is a necessary step for complete analysis. Conventional analysis methods take several days, whereas it takes a few hours in microwave digestion method. In the present study, a new wet chemical analysis technique was developed and applied to quantify the sulfide content in Turkish coal samples. In the initial step, 5 M HCl solution is employed to dissolve the sulfate phases present in the coal. Subsequently, pyrite is separated from the residue obtained in stage 1 through the utilization of 2 M HNO3 solution. The final stage involves the determination of organic sulfur, wherein concentrated HNO3, HCl, HF, and boric acid are utilized to completely decompose the remaining residue from stage 2. The extracted solutions from each stage are promptly analyzed for sulfur content using ICP-OES. The cumulative values of the three sulfur forms consistently correspond with certified total sulfur data for the majority of the examined coals. Among several advantages, the analysis duration is the most distinctive feature. Chemical analysis of sulfide in coal with microwave analysis method completed three times rapid than with conventional analysis.

Strategies for Sustainable Water Management

Groundwater in the Rania basin, Iraqi Kurdistan region, has been under intensive exploitation in the last two decades, where quantity and quality are both affected. Hence, any attempt to protect the aquifers has become an urgent need. Saruchawa, Qulai Rania, and Qulai Kanimaran are the three large springs, among dozens of others in the area, that are heavily relied on as the sole or main source of water supply. Hydrochemical analysis, the first and most practical step to evaluating the water quality, was carried out through 60 water samples collected from 13 springs and 17 wells in both dry and wet seasons (October 2018 and May 2019). Laboratory results show a high calcium bicarbonate concentration with weak acids’ dominance. Protection zones are delineated for these springs using aquifer susceptibility to contamination and analysis of the recession part of the spring curves. The equivalent relationship between the protection factor (Fp) produced by the Epikarst, protective cover, infiltration condition, and Karst network development mapping method and the groundwater protection zone (S) is considered. Qulai Rania and Kanimaran Springs are mapped to be in S2 (a highly vulnerable area), whereas Saruchawa Spring is located in S1 (very highly vulnerable). Based on the second method results (recession curve analyses), all three selected springs fall under the (D-type) vulnerability category. As a result, the immediate protection zone was going to be surrounded by the inner protection zone, and both are enclosed within the outer protection zone, which covers the remainder of the catchment area.

Mapping the Path of Digital Transformation: The Role of Leadership in Building Adaptive Organizational Culture

The development movement continues towards the digital transformation era, marked by rapid advances in information and computer technology (ICT) and the emergence of social media platforms that change the order of life to be more practical and accessible and deny the geographical distance between people. These changes require universities to adapt to the demands of rapid development. This study aims to explore the strategies carried out by university leaders in facing the challenges of the digital era and map the extent to which the transformation of university organizational culture is operated in the face of digital technology pressure. The research used a qualitative approach with a multi-site format in three private universities. Data analysis applies constant comparative analysis, which ends with a simultaneous analysis step. The results showed that university leaders are ready to implement an open strategy in facing the challenges of the digital era by utilizing these technological advances. However, there are differences in the utilization of network assets owned by institutions and the knowledge and experience of leaders. Organizational culture transformation is operated to the extent that institutions can master digital technology to optimize the performance of institutional services. Universities' engagement with digital technology does not disrupt the resilience of their organizational culture because it only leads to an adaptive surface layer.

Feature selection by replicate reproducibility and non-redundancy.

A fundamental step in many analyses of high-dimensional data is dimension reduction. Two basic approaches are introduction of new synthetic coordinates and selection of extant features. Advantages of the latter include interpretability, simplicity, transferability, and modularity. A common criterion for unsupervized feature selection is variance or dynamic range. However, in practice, it can occur that high-variance features are noisy, that important features have low variance, or that variances are simply not comparable across features because they are measured in unrelated numeric scales or physical units. Moreover, users may want to include measures of signal-to-noise ratio and non-redundancy into feature selection. Here, we introduce the RNR algorithm, which selects features based on (i) the reproducibility of their signal across replicates and (ii) their non-redundancy, measured by linear dependence. It takes as input a typically large set of features measured on a collection of objects with two or more replicates per object. It returns an ordered list of features, i1,i2,…,ik, where feature i1 is the one with the highest reproducibility across replicates, i2 that with the highest reproducibility across replicates after projecting out the dimension spanned by i1, and so on. Applications to microscopy-based imaging of cells and proteomics highlight benefits of the approach. The RNR method is available via Bioconductor (Huber W, Carey VJ, Gentleman R et al. (Orchestrating high-throughput genomic analysis with bioconductor. Nat Methods 2015;12:115-21.) in the R package FeatSeekR. Its source code is also available at https://github.com/tcapraz/FeatSeekR under the GPL-3 open source license.

VICTOR: Validation and inspection of cell type annotation through optimal regression

Single-cell RNA sequencing provides unprecedent opportunities to explore the heterogeneity and dynamics inherent in cellular biology. An essential step in the data analysis involves the automatic annotation of cells. Despite development of numerous tools for automated cell annotation, assessing the reliability of predicted annotations remains challenging, particularly for rare and unknown cell types. Here, we introduce VICTOR: Validation and inspection of cell type annotation through optimal regression. VICTOR aims to gauge the confidence of cell annotations by an elastic-net regularized regression with optimal thresholds. We demonstrated that VICTOR performed well in identifying inaccurate annotations, surpassing existing methods in diagnostic ability across various single-cell datasets, including within-platform, cross-platform, cross-studies, and cross-omics settings.

Semantic Segmentation of Point Cloud Scene via Multi-Scale Feature Aggregation and Adaptive Fusion

Point cloud semantic segmentation is a key step in 3D scene understanding and analysis. In recent years, deep learning–based point cloud semantic segmentation methods have received extensive attention from researchers. Multi-scale neighborhood feature learning methods are suitable for inhomogeneous density point clouds, but different scale branching feature learning increases the computational complexity and makes it difficult to accurately fuse different scale features to express local information. In this study, a point cloud semantic segmentation network based on RandLA-Net with multi-scale local feature aggregation and adaptive fusion is proposed. The designed structure can reduce computational complexity and accurately express local features. The mean intersection-over-union is improved by 1.1% on the SemanticKITTI data set with an inference speed of nine frames per second, while the mean intersection-over-union is improved by 0.9% on the S3DIS data set, compared with RandLA-Net. We also conduct ablation studies to validate the effectiveness of the proposed key structure.

Automated echocardiography view classification and quality assessment with recognition of unknown views.

Interpreting echocardiographic exams requires substantial manual interaction as videos lack scan-plane information and have inconsistent image quality, ranging from clinically relevant to unrecognizable. Thus, a manual prerequisite step for analysis is to select the appropriate views that showcase both the target anatomy and optimal image quality. To automate this selection process, we present a method for automatic classification of routine views, recognition of unknown views, and quality assessment of detected views. We train a neural network for view classification and employ the logit activations from the neural network for unknown view recognition. Subsequently, we train a linear regression algorithm that uses feature embeddings from the neural network to predict view quality scores. We evaluate the method on a clinical test set of 2466 echocardiography videos with expert-annotated view labels and a subset of 438 videos with expert-rated view quality scores. A second observer annotated a subset of 894 videos, including all quality-rated videos. The proposed method achieved an accuracy of for the joint objective of routine view classification and unknown view recognition, whereas a second observer reached an accuracy of 87.6%. For view quality assessment, the method achieved a Spearman's rank correlation coefficient of 0.71, whereas a second observer reached a correlation coefficient of 0.62. The proposed method approaches expert-level performance, enabling fully automatic selection of the most appropriate views for manual or automatic downstream analysis.

Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble.

We investigate the ability to discover data assimilation (DA) schemes meant for chaotic dynamics with deep learning. The focus is on learning the analysis step of sequential DA, from state trajectories and their observations, using a simple residual convolutional neural network, while assuming the dynamics to be known. Experiments are performed with the Lorenz 96 dynamics, which display spatiotemporal chaos and for which solid benchmarks for DA performance exist. The accuracy of the states obtained from the learned analysis approaches that of the best possibly tuned ensemble Kalman filter and is far better than that of variational DA alternatives. Critically, this can be achieved while propagating even just a single state in the forecast step. We investigate the reason for achieving ensemble filtering accuracy without an ensemble. We diagnose that the analysis scheme actually identifies key dynamical perturbations, mildly aligned with the unstable subspace, from the forecast state alone, without any ensemble-based covariances representation. This reveals that the analysis scheme has learned some multiplicative ergodic theorem associated to the DA process seen as a non-autonomous random dynamical system.

Semantic Deviation in W. Wordsworth's Selected Poems

The study investigates and analyzes W. Wordsworth's four selected poems on the basis of G. Leech's taxonomy of semantic deviation (1969). The significance of the analysis is to disclose to what extent the poet uses such deviation in his literary texts and how semantic deviation serves the meaning of the poems. The analysis samples are 'The Daffodils', 'My Heart Leaps Up', 'The Solitary Reaper' and 'Composed upon Westminster Bridge', respectively. Leech's classification of semantic deviation (1969), as the model of the study, is utilized to analyze them. The procedures of the study ,that are utilized to conduct the current study analysis, starts with reading and investigating the semantic deviation in these selected poems. Then, the step of analysis is conducted in the light of the study model, Leech's classification of semantic deviation (1969). Finally, the findings of the data analysis are discussed and it is followed by the study conclusion. The results have revealed that semantic deviation is highly utilized in these selected poems so as to serve and diversify their meanings and to be more attractive on the part of the readers. Transference of meaning, a type of semantic deviation, is the most dominant type that is used in these poems in comparison with the others.

Multipartite network analysis to identify environmental and genetic associations of metabolic syndrome in the Korean population

Network analysis has become a crucial tool in genetic research, enabling the exploration of associations between genes and diseases. Its utility extends beyond genetics to include the assessment of environmental factors. Unipartite network analysis is commonly used in genomics to visualize initial insights and relationships among variables. Syndromic diseases, such as metabolic syndrome, are characterized by the simultaneous occurrence of various signs, symptoms, and clinicopathological features. Metabolic syndrome encompasses hypertension, diabetes, obesity, and dyslipidemia, and both genetic and environmental factors contribute to its development. Given that relevant data often consist of distinct sets of variables, a more intuitive visualization method is needed. This study applied multipartite network analysis as an effective method to understand the associations among genetic, environmental, and disease components in syndromic diseases. We considered three distinct variable sets: genetic factors, environmental factors, and disease components. The process involved projecting a tripartite network onto a two-mode bipartite network and then simplifying it into a one-mode network. This approach facilitated the visualization of relationships among factors across different sets and within individual sets. To transition from multipartite to unipartite networks, we suggest both sequential and concurrent projection methods. Data from the Korean Association Resource (KARE) project were utilized, including 352,228 SNPs from 8840 individuals, alongside information on environmental factors such as lifestyle, dietary, and socioeconomic factors. The single-SNP analysis step filtered SNPs, supplemented by reference SNPs reported in a genome-wide association study catalog. The resulting network patterns differed significantly by sex: demographic factors and fat intake were crucial for women, while alcohol consumption was central for men. Indirect relationships were identified through projected bipartite networks, revealing that SNPs such as rs4244457, rs2156552, and rs10899345 had lifestyle interactions on metabolic components. Our approach offers several advantages: it simplifies the visualization of complex relationships among different datasets, identifies environmental interactions, and provides insights into SNP clusters sharing common environmental factors and metabolic components. This framework provides a comprehensive approach to elucidate the mechanisms underlying complex diseases like metabolic syndrome.

Eufemism and Dyphemisms in the Comments Column Youtube Serambinews

<span lang="EN-US">This research is titled Euphemisms and Dysphemism in the Serambinews YouTube Comment Section. The method used is a descriptive research method with a qualitative approach. The data of this study are all comments that contain euphemisms and dysphemism with the source of netizens' comment data in the Serambinews YouTube comment column. The number of data studied in this study was 105 consisting of 34 euphemisms and 71 dysphemisms. This study used to read and recording techniques. The data analysis steps of this study include data selection, data classification, data analysis, and formulating conclusions. The results showed that the use of euphemisms and dysphemisms was due to the context behind them. Generally, euphemisms are used to be more fun; not offensive and dysphemism is used to mock, to solicit, to comment and to frown, to express disappointment, to show anger, to repeat requests, to ask, to blame, to respond to criticism, and to joke. The function of euphemisms found in this study is a protective function used to avoid words that can cause problems, conflicts, dangers, emotions, anger, hurt feelings, insults, shame, blasphemy, swearing, taboos, profanity, disgusting, and avoiding words that can cause panic. On the contrary, the function of dysphemism to demean or express contempt, to show dislike, also disapproval of someone or something, to express anger and irritation shows an aggressive or provocative nature, and to make fun of the ruler</span>

The Role of Teachers in Improving the Quality of Education at MA Darul Ulum Semarang

This article aims to describe the role of gudu in improving the quality of education at MA Darul Ulum Semarang, Ngaliyan District, Semarang City. This research uses qualitative research, with a naturalistic approach, research data collection is obtained using observation, interview, and documentation techniques. The data analysis steps carried out were using the Bogdan model analysis, namely reducing the data, presenting the data, and then concluding. As a result of this research, researchers found four findings including: 1) The role of teachers in improving the quality of education at MA Darul Ulum is that teachers who teach must be linear with their field. 2) Teachers prepare complete learning tools at MA Darul Ulum before starting learning 3) Teachers at MA Darul Ulum accept technological developments consciously so that they help to develop the quality of student education 4) Facilities and infrastructure are a challenge for teachers at MA Darul Ulum to improve quality of education

Optimising microplastics analysis for quantifying and identifying microplastic fibres in laundry wastewater

Current methods for measuring microplastic fibres (MPF) are cumbersome, time consuming and unscalable for routine high throughput analysis. This study reports a method for rapidly extracting, quantifying and analysing MPFs in laundry wastewater with several key improvements which vastly enhance overall efficiency and scalability of analysis. FT-IR surveying is employed as a preliminary step in analysis to quickly determine what polymers are present in a sample prior to fluorescence treatment. Using random quadrating, whole 25 mm filter membranes were surveyed in <30 min with high recovery rates. In industrial laundry wastewater samples, polyester was the most common MPF, however acrylic, nylon, cotton and rayon were all ubiquitous. The study also demonstrates that an excitation wavelength of 365 nm was optimal for fluorescing PET fibres like polyester which were stained with Nile Red, but not 495 nm, which is commonly used in microplastic analysis. Finally, a custom ImageJ macro was written to automatically enumerate and describe MPFs on filter membranes using just a single stitched fluorescence image. In just a few seconds, concentrations of up to 40,000 fibres/L were analysed in industrial laundry wastewater samples with a lower particle size limit of 20 μm. This study highlights the need for more optimised and scalable analysis workflows which maintain high levels of reliability and accuracy.

CNTools: A computational toolbox for cellular neighborhood analysis from multiplexed images.

Recent studies show that cellular neighborhoods play an important role in evolving biological events such as cancer and diabetes. Therefore, it is critical to accurately and efficiently identify cellular neighborhoods from spatially-resolved single-cell transcriptomic data or single-cell resolution tissue imaging data. In this work, we develop CNTools, a computational toolbox for end-to-end cellular neighborhood analysis on annotated cell images, comprising both the identification and analysis steps. It includes state-of-the-art cellular neighborhood identification methods and post-identification smoothing techniques, with our newly proposed Cellular Neighbor Embedding (CNE) method and Naive Smoothing technique, as well as several established downstream analysis approaches. We applied CNTools on three real-world CODEX datasets and evaluated identification methods with smoothing techniques quantitatively and qualitatively. It shows that CNE with Naive Smoothing overall outperformed other methods and revealed more convincing biological insights. We also provided suggestions on how to choose proper identification methods and smoothing techniques according to input data.

Exploring High School Economics Teachers’ Curriculum Implementation Practices: A Case of the Northern Cape Province of South Africa

This empirical study explored high school Economics teachers’ curriculum implementation practices in South Africa. The research focused on a sample of three Economics teachers purposefully selected from three schools in the Northern Cape province. Drawing on the theoretical framework of Mediated Learning Experience (MLE) and adopting a qualitative approach, the study employed a case study design.To get insight on the Economics teachers’ curriculum practices, semi-structured interviews, classroom observations, and document analysis were used to generate data from the selected high school Economics teachers. Braun and Clarke’s six steps of thematic analysis were followed in analysing the triangulated data. The study findings indicate that some Economics teachers’ practices on instructional planning, selection of resources, feedback, and reflective practices promoted meaningful learning. The study concludes that although there are pockets of effective curriculum implementation practices by Economics teachers, there is a need to continue with in-service teacher training to enhance the teachers’ curriculum implementation practices. The study recommends that Economics teachers should form Professional Learning Communities where they learn and share good practices to provide teachers with continuous in-service training on how to plan lessons that cater for diverse learners’ needs. This study contributes to the scholarship by providing insights into how teachers’ curriculum practices can be enhanced both at pre-service and in-service levels to improve teacher effectiveness and efficiency. Furthermore, the study forms the basis on which further research on Economics teachers’ curriculum implementation practices on a more representative scale can be conducted. Keywords: Economics Education, Curriculum Implementation, Learner-centred Practices, Mediated Learning Experience

Enhancing Independence: Non-Immersive Virtual Reality for Teaching Cooking Skills to High School Students With Intellectual Disability

Virtual reality (VR) is a promising avenue to enhance the independence and daily living skills of high school students with intellectual disability. This study investigated the efficacy of a non-immersive VR focused on teaching food preparation skills to secondary students with intellectual disability. Three high school students with intellectual disability participated in a multiple probe across participants single case design study, in which they engaged with non-immersive VR to learn to cook a grilled cheese sandwich on a stove. Researchers found students improved their accuracy with cooking a grilled cheese sandwich on a stove following intervention as measured by task analysis steps on a delayed probe. Students also maintained high levels of accuracy after the intervention ended. Researchers determined non-immersive VR an effective and efficient intervention for secondary students with intellectual disability learning daily living skills.