Results Of Hierarchical Clustering Research Articles

Understanding public sentiments and misbeliefs about Sustainable Development Goals: a sentiment and topic modeling analysis

PurposeMisinformation surrounding the Sustainable Development Goals (SDGs) has contributed to the formation of misbeliefs among the public. The purpose of this paper is to investigate public sentiment and misbeliefs about the SDGs on the YouTube platform.Design/methodology/approachThe authors extracted 8,016 comments from YouTube videos associated with SDGs. The authors used a pre-trained Python library NRC lexicon for sentiment and emotion analysis, and to extract latent topics, the authors used BERTopic for topic modeling.FindingsThe authors found eight emotions, with negativity outweighing positivity, in the comment section. In addition, the authors identified the top 20 topics discussing various SDGs and SDG-related misbeliefs.Practical implicationsThe authors reported topics related to public misbeliefs about SDGs and associated keywords. These keywords can be used to formulate social media content moderation strategies to screen out content that creates these misbeliefs. The result of hierarchical clustering can be used to devise and optimize response strategies by governments and policymakers to counter public misbeliefs.Originality/valueThis study represents an initial endeavor to gain a deeper understanding of the public’s misbeliefs regarding SDGs. The authors identified novel misbeliefs about SDGs that previous literature has not studied. Furthermore, the authors introduce an algorithm BERTopic for topic modeling that leverages transformer architecture for context-aware topic modeling.

Effects of combined microplastic and cadmium pollution on sorghum growth, Cd accumulation, and rhizosphere microbial functions

The interaction between microplastics (MPs) and cadmium (Cd) poses a threat to agricultural soil environments, and their effects on plant growth and rhizosphere microbial community functions are not yet clear. In this study, energy sorghum was used as a test plant to investigate the effects of two types of MPs, polystyrene (PS) and polyethylene (PE), at different particle sizes (13 μm, 550 μm) and concentrations (0.1%, 1% w/w), and Cd, as well as their interactions, on the growth of sorghum in a soil-cultivation pot experiment. The results showed that the combined effects of MP and Cd pollution on the dry weight and Cd accumulation rate in sorghum varied depending on the type, concentration, and particle size of the MPs, with an overall trend of increasing stress from combined pollution with increasing Cd content and accumulation. High-throughput sequencing analysis revealed that combined MP and Cd pollution increased bacterial diversity, and the most significant increase was observed in the abundance-based coverage estimator (ACE), Shannon, and Sobs indices in the 13 μm 1% PS+Cd treatment group. Metagenomic analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways revealed that 19 groups of metabolic pathways, including microbial metabolism and methane metabolism, differed significantly under combined MP and Cd pollution. Hierarchical clustering results indicated that Cd treatment and combined MP and Cd treatment affected the abundances of sorghum rhizosphere soil nitrogen (N) and phosphorus (P) cycling genes and that the type of MP present was an important factor affecting N and P cycling genes. The results of this study provide a basis for exploring the toxic effects of combined MP and Cd pollution and for conducting soil environmental risk assessments.

DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction.

Anti-cancer response of cell lines to drugs is in urgent need for individualized precision medical decision-making in the era of precision medicine. Measurements with wet-experiments is time-consuming and expensive and it is almost impossible for wide ranges of application. The design of computational models that can precisely predict the responses between drugs and cell lines could provide a credible reference for further research. Existing methods of response prediction based on matrix factorization or neural networks have revealed that both linear or nonlinear latent characteristics are applicable and effective for the precise prediction of drug responses. However, the majority of them consider only linear or nonlinear relationships for drug response prediction. Herein, we propose a Dual Branch Deep Neural Matrix Factorization (DBDNMF) method to address the above-mentioned issues. DBDNMF learns the latent representation of drugs and cell lines through flexible inputs and reconstructs the partially observed matrix through a series of hidden neural network layers. Experimental results on the datasets of Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) show that the accuracy of drug prediction exceeds state-of-the-art drug response prediction algorithms, demonstrating its reliability and stability. The hierarchical clustering results show that drugs with similar response levels tend to target similar signaling pathway, and cell lines coming from the same tissue subtype tend to share the same pattern of response, which are consistent with previously published studies.

Abstract 2323: Deciphering RNA degradation: Insights from a comparative analysis of paired fresh frozen/FFPE total RNA-seq

Abstract Background: Fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE) samples are primary resources for archival tissues in cancer studies. Despite the advantages of easy storage and cost-effectiveness, FFPE samples have the disadvantage of inevitable chemical-induced RNA degradation. Although, in mRNA-seq data, the 3' bias due to the characteristics of the mRNA-seq platform allows the measure of RNA degradation levels, for total RNA-seq and FFPE samples, there is still no clear measure for RNA degradation. Methods: Our study, utilizing The Cancer Genome Atlas (TCGA) pilot data comprised of 26 paired samples of fresh frozen mRNA-seq [FFM], fresh frozen total RNA-seq [FFT], and FFPE total RNA-seq [PET], investigated RNA degradation patterns in FFPE samples. Upon investigating the read coverage of transcripts in FFPE data, we observed increased noise parameters in selected samples compared to others, suggesting a method for assessment of RNA degradation in FFPE. To measure these noise patterns, we developed a method called windowCV (wCV) utilizing coefficient of variance (CV) along the transcript length. Also, based on reports that membrane-surrounded RNA such as lncRNA and mitochondrial RNA (mtRNA) might be less affected by formalin fixation, we developed the simple measure dividing expression value of other proteins with certain lncRNA or mtRNA inferring the degree of RNA degradation. Results: We first compared the shape of transcript read coverage from 3 different RNA-seq data and observed 3’bias pattern in FFM which was not observed in FFT and PET. For FFT and PET, we applied wCV and executed hierarchical clustering. We observed that a subset of samples and genes were clustered by high wCV value suggesting notable RNA degradation. To support the hypothesis of RNA degradation, we utilized Trimmed Mean of M values (TMM) expression ratio of paired FFT and PET to determine which samples and genes were associated with underestimation of expression in PET. Interestingly, we observed that PET/FFT TMM ratio showed negative correlation with wCV. Additionally, when we investigated hierarchical clustering result of PET/FFT TMM ratio, we found that lncRNA and mtRNA were less affected by FFPE. We then calculated expression ratio between membrane-surrounded RNA and a set of protein coding genes largely affected by FFPE. As a result, we found that this ratio positively correlated with paired PET/FFT TMM ratio. We conclude that this simple measurement can provide information of FFPE induced underestimated expression with limited transcriptome data as well as RNA degradation. Conclusion: Our method can provide gene-level CV while avoiding false positive variance signal originating from transcript model and simple way to detect the degree of RNA degradation across FFPE samples. Citation Format: Wonyoung Choi, Miyeon Yeon, Hyo Young Choi, David Neil Hayes. Deciphering RNA degradation: Insights from a comparative analysis of paired fresh frozen/FFPE total RNA-seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2323.

Quantitative and qualitative assessment of groundwater resources for drinking water supply in the peri-urban area of Dhaka, Bangladesh

Groundwater is the primary source of freshwater for drinking, industrial, and agricultural water in many nations worldwide. This vital resource is deteriorating due to rapidly growing urbanization, pollution, overexploitation, and climate change. However, it is unclear how much this specific resource deteriorates over time. Therefore, this study aimed to investigate groundwater resources qualitatively and quantitatively in the peri-urban area of Dhaka, Bangladesh. The study used the modified Mann-Kendall trend test, hotspot analysis, groundwater recharge estimation, multivariate statistical analysis, and the water quality index (WQI) mapping. The trend analysis of groundwater levels indicates that in the most recent year, groundwater levels close to industrial areas have declined at a maximum rate of 1.18 m/year. The estimated groundwater recharge reveals that it is highest (480 mm/year) on rural agricultural land and lowest (220 mm/year) in urban areas. With a 99% confidence level, the groundwater water trend cluster analysis indicates the hotspots of groundwater level depletion near the industrial area. The results showed that Mn, Fe, and As exceeded the World Health Organization's (WHO) recommended limit for drinking water by 76.92%, 61.53%, and 23.07%, respectively. The principal component analysis and hierarchical clustering results indicate that groundwater quality is influenced by rock-water interaction and human interventions. The WQI results suggest that 47.05% of the samples were excellent, 7.69% were good, 9.96% were poor, 17.65% were very poor, and the remaining 17.65% were unfit for drinking purposes. The depletion of groundwater hotspots was identified in northeast region of the study area. Overall, an integrated framework was proposed that offers a holistic approach to sustainable groundwater resource management amid urbanization and industrialization. These findings will provide an essential guideline for groundwater resource management in the peri-urban area of Dhaka city.

Impact of Dataset Scaling on Hierarchical Clustering: A Comparative Analysis of Distance-Based and Ratio-Based Methods

In this study, the distance-based agglomerative hierarchical clustering techniques were compared to a ratio-based approach. Two real datasets, which were also used in a prior study by Roux (2018), were considered. Firstly, it was observed that the type of scaling applied to the datasets was found to affect the results of hierarchical clustering. Thus, various scaling methods were employed prior to implementing hierarchical clustering. Furthermore, two rank-based goodness-of-fit measures were used to evaluate the hierarchical clustering methods. In contrast to Roux (2018) findings, it was observed that the distance-based methods, such as Median linkage, Average linkage, and centroid linkage, performed better than the ratio-based method. The ratio-based methods also showed issues with branch crossing in the hierarchical clustering dendrogram. Consequently, this study illustrates that, with appropriate dataset scaling, the distance-based methods outperform ratio-based methods in terms of goodness-of-fit measures.

Association of verbal and non-verbal theory of mind abilities with non-coding variants of OXTR in youth with autism spectrum disorder and typically developing individuals: a case-control study

BackgroundThe ability to attribute mental states to others is called theory of mind (ToM) and is a substantial component of social cognition. This ability is abnormally developed in individuals with autism spectrum disorder (ASD). Several studies over the past decade have identified the oxytocin receptor gene (OXTR) and its variants as promising components for explaining the molecular mechanisms underlying Theory of Mind (ToM). The main aim of this study is to examine the association between rs2268498 and rs53576, two functional single nucleotide polymorphisms (SNPs), and verbal and non-verbal ToM in children and adolescents with ASD and a group of typically developing youth.MethodsThe study involved 44 children and adolescents with high-functioning ASD aged 8 to 18 years old and 44 TD individuals who were matched on age and sex. In all participants, blood samples were collected and rs2268498 and rs53576 were genotyped. Happe’s Strange Stories test and the moving shapes paradigm were used to measure verbal and non-verbal ToM in all participants.ResultsThe results of permutation tests and logistic regression suggested that in TD group, rs2268498 AA carriers showed significant higher scores in variables representing verbal ToM (ToM stories and appropriateness score) whereas, in ASD group, rs53576 AA carriers exhibited significant better performance in parameters related to non-verbal ToM (ToM general rule and intentionality score). The results of hierarchical clustering in both groups support the findings by distinguishing between language-related and language-independent aspects of ToM.ConclusionsIn the present study, we examined the association between rs2268498 and rs53576 and social functioning in individuals with ASD and TD group. We found preliminary evidence that rs2268498 and rs53576 are associated with ToM related abilities in healthy individuals as well as in autistic individuals. Accordingly, rs2268498 and rs53576 may play an important role in predicting ToM capabilities. It will be necessary to conduct further research to address the association of genetic variants with a deficit in ToM in individuals with ASD.

Investigating the impact of attenuated fluorescence spectra on protein discrimination.

The optical remote sensing techniques are promising for the real-time detection, and identification of different types of hazardous biological materials. However, the received fluorescent spectra from a remote distance suffer from the atmospheric attenuation effect upon the spectral shape. To investigate the influence of atmospheric attenuation on characterizing, and classifying biological agents, the laboratory-measured fluorescence data of fourteen proteins combined with the atmospheric transmission factors of the MODTRAN model were conducted with different detection ranges. The multivariate analysis techniques of principal component analysis (PCA) and linear discriminant analysis (LDA), and the predictors of Random Forest and XGBoost were employed to assess the separability and distinguishability of different spectra recorded. The results showed that the spectral-shift effect on attenuated spectra varied as a function of the detection range, the atmospheric visibility, and the spectral distribution. According to the PCA and LDA analysis, the distribution of decomposed factors changed in the spectral explanatory power with the increasing attenuation effect, which was consistent with the hierarchical clustering results. Random Forest exhibited higher performance in classifying protein samples than that of XGBoost, while the two methods performed similarly in identifying harmful and harmless subgroups of proteins. Fewer subgroups decreased the sensitivity of the classification accuracy to the attenuation effect. Our analysis demonstrated that combining atmospheric transport models to build a fluorescence spectral database is essential for fast identification between spectra, and reduced classification criteria could facilitate the compatibility of spectral database and classification algorithms.

Multi-attribute hierarchical clustering for product family division of customized wooden doors

To improve the production system for customized wooden doors and to gain research and development efficiency, this paper proposed the feasibility of using hierarchical clustering algorithms to cluster a company’s customized wooden door products and its application to rational product family architecture. The particular use of multi-attribute feature data to locate products and the integration of image data into the database can make the original hierarchical clustering more compatible and adaptable for application to customized wooden doors. The preprocessed data was analyzed by clustering to obtain the clustering results and similarity relationships. Hierarchical clustering results were uneven and not entirely interpreted. However, the internal order structure of clusters and the clustering process could be clearly observed, and the distance hierarchical relationship between the products could be obtained, which was beneficial to the division of the product architecture. The results illustrated that processing using hierarchical clustering of multi-attribute data is feasible for optimizing customized wooden door product systems. In addition, the product architecture, product coding rules, and front-end development process were established to improve standardization and research and development efficiency. There is still great potential for developing the custom wooden door category in custom furniture companies.

Unsupervised Learning of non‐Hermitian Photonic Bulk Topology

AbstractMachine‐learning has proven useful in distinguishing topological phases. However, there is still a lack of relevant research in the non‐Hermitian community, especially from the perspective of the momentum‐space. Here, an unsupervised machine‐learning method, diffusion maps, is used to study non‐Hermitian topologies in the momentum‐space. Choosing proper topological descriptors as input datasets, topological phases are successfully distinguished in several prototypical cases, including a line‐gapped tight‐binding model, a line‐gapped Floquet model, and a point‐gapped tight‐binding model. The datasets can be further reduced when certain symmetries exist. A mixed diffusion kernel method is proposed and developed, which could study several topologies at the same time and give hierarchical clustering results. As an application, a novel phase transition process is discovered in a non‐Hermitian honeycomb lattice without tedious numerical calculations. This study characterizes band properties without any prior knowledge, which provides a convenient and powerful way to study topology in non‐Hermitian systems.

Identifying interactions of linked irrigated lake-groundwater system by combining hydrodynamic and hydrochemical method.

During the irrigation period, the interactions between the linked lake-groundwater systems are complicated and change. This is because natural and human activities are happening at the same time, which makes it harder to identify the interactions. This study uses data on water level, hydrochemistry, and hydrogen-oxygen stable isotopes to analyze the hydrodynamics, electrical conductivity (EC), isotopic characteristics, and spatial distribution of lake water and groundwater to reveal lake-groundwater interactions. The results indicate that the hydrochemical type of Chagan Lake and groundwater is dominated by the HCO3-Na type. The key hydrochemical indicator EC obtained by principal component analysis (PCA) can be used to reveal the lake-groundwater interaction, and the interaction should be identified by location according to the significant correlation between hierarchical clustering results and regional distribution. The lake body's geographic coefficient of variation for EC and δ18O is small, and irrigation return flow is one factor in the region's surface water's significant spatial variation for EC and δ18O. The three study methods indicate that the groundwater supplies the lake in the vicinity of the Huoling River-Hongzi Pool, while in other sections, the lake water leaks and replenishes the groundwater, exhibiting geographic inconsistency. The isotope method was employed as a support tool to determine that groundwater might recharge the lake at Xinmiao Pool. According to the calculations of the Mix SIAR model, the groundwater recharge contribution rate in the Xinmiao Pool section is approximately 51%, while in the remaining sections, the contribution rate of lake water to groundwater ranges from approximately 25% to 52%. Therefore, the identification of the interaction is crucial for the linked irrigated lake-groundwater system where water sources are scarce and threatened by agricultural pollution.

Uncovering the phenolic diversity of Guabiju fruit: LC-MS/MS-based targeted metabolomics approach

The comprehensive composition of phenolic compounds (PC) from seven genotypes of guabiju were analyzed by high-performance liquid chromatography coupled to a diode array detector and mass spectrometry (HPLC-ESI-qTOF-MS/MS), and a targeted metabolomic approach was utilized to explore the PC-related similarities among the genotypes. Sixty-seven phenolic compounds were annotated and twenty-four were quantified in all genotypes of guabiju. The phenolic acids and anthocyanins were the major PC, representing more than 63% (w/w) of the total PC. Di-O-galloylquinic and tri-O-galloylquinic acids and ellagitannins were reported for the first time in guabiju. The results of hierarchical clustering and principal components analysis (PCA) suggested seven groups as suitable clusters to be formed according to phenolic composition. Eleven PC were selected as relevant for sample clustering, and six of them were highlighted as the most informative (in decreasing order of importance): epicatechin, catechin, (epi)gallocatechin gallate II, di-O-galloylquinic acid I, tri-O-galloylquinic acid and delphinidin 3-O-glucoside. To the best of our knowledge, this study contributes to the literature with the most complete phenolic profile of guabiju genotypes up to date. Moreover, guabiju susceptibility to fungal infestation related to PC composition was briefly discussed based on a parallel study using the same genotypes.

Explore variation of salivary bacteria across time and geolocations.

Saliva is an informative body fluid that can be found at various crime scenes, and the salivary bacterial community has been revealed it is a potential auxiliary target for forensic identification. However, the variation of salivary bacterial community composition across time and geolocation needs to be explored. The study was designed to be carried out during the winter vacation that was across about 50days and eight geographic locations. The high throughput sequencing was performed with the V3-V4 region of the16S rRNA gene to explore salivary bacterial community composition. An overall slight fluctuation of the salivary bacteria was observed, which primarily occurred in the relative abundance of the salivary bacterial taxa. The results of principal coordinate analysis and hierarchical clustering showed samples were clustered by the individuals. All individuals could be correctly identified with the random forest model. In summation, although the relative abundance of salivary bacteria varied across the changes of time and geolocation, the individualized characteristic of salivary bacteria remained steady, which is beneficial for the salivary bacterial application in personal identification.

Indications of marine benthos occurrence from multi-spectral multi-beam backscatter data: a case study in the North Sea

To facilitate the conservation of seafloor habitats and planning of offshore activities, there is a growing need for mapping marine benthos in an effective and efficient way. Acoustic data acquired by multi-beam echosounders (MBES) have been extensively used for large-scale and high-resolution seafloor characterization. A deeper understanding of the relationship between backscatter data and sediment compositions can help to identify the benthos occurrence from the MBES data. With two multi-spectral MBES datasets collected near the western Wadden Sea islands in the North Sea, we investigated the potential of mapping marine benthos through backscatter classification. Two unsupervised classification methods, i.e., Bayesian classification, which mainly exploits the backscatter strength from incident angles larger than 20°, and hierarchical clustering of the backscatter strength at different angular ranges, were employed and the results were compared. The classification results from both methods showed a good correspondence with sediment properties such as the median grain size. Moreover, based on a principal component analysis of bottom sample properties, the hierarchical clustering results indicated a better distinction between contributions from the gravel content and benthos occurrence, e.g., sand mason worm density, than Bayesian classification, through involving the backscatter angular variations. Classification for multiple frequencies, on the other hand, showed little difference regarding the relationship with bottom sample properties. Although the backscatter difference between frequencies was also found to positively correlate with certain sample properties, using multi-spectral features for acoustic classification in this study did not reveal additional information compared to single-frequency classification results.

A brain-wide genome-wide association study of candidate quantitative trait loci associated with structural and functional phenotypes of pain sensitivity.

Individual pain sensitivity is modulated by the brain's structural and functional features, but its heritability remains unclear. This paper conducted a brain-wide genome-wide association study (GWAS) to explore the genetic bases of neuroimage phenotypes of pain sensitivity. In total, 432 normal participants were divided into high and low pain sensitivity groups according to the laser quantitative test threshold. Then, the brain's gray matter density (GMD) features correlated with pain sensitivity were identified. Next, GWAS was performed on each GMD phenotype using quality-controlled genotypes. Based on the heatmap and hierarchical clustering results, the right insula was identified for further refined analysis in terms of subregions GMD and resting-state functional connectivity (rs-FC) phenotypes. The results indicate that the right insula GMD in the high sensitivity group is significantly lower than that in the low sensitivity group. Also, the TT/TC group at locus rs187974 has lower right insula GMD than the CC group. Further, loci at gene CYP2D6 may lead to a variation of rs-FC between the right insula and left putamen. In conclusion, our study suggests that the right insula and multiple candidate loci may be importantly involved in pain sensitivity modulation, which may guide the future development of precision pain therapeutics.

Feasibility Assessment of the Identification of the Source of Condensed Tannins in Leathers by FTIR Spectroscopy and Chemometrics

This study aimed to investigate the feasibility of identifying the type of plant used in the tanning of leathers by cost-effective Fourier transform infrared (FTIR) spectroscopy. The investigation was performed on European horse-chestnut (fruit peel), mimosa, and quebracho and three specimens of mimosa-tanned leathers. Tannin extraction from plants was performed in an ultrasonic bath using acetone-water solvent (70%). Tannin extraction from leathers was carried out from the corium fibers using acetone-water solvent (1:1). After extraction, filtration, centrifugation, and solvent removal, the samples were subjected to FTIR spectroscopy. Principal Component Analysis (PCA) and hierarchical clustering were used to identify the source of tannins based on FTIR results. In addition to FTIR spectra, their first and second derivatives were also used in statistical analyses. The obtained FTIR spectra and their derivatives and the results of PCA and hierarchical clustering showed that rich plant sources of condensed tannins can be well differentiated by spectroscopy in the fingerprint region (700-1800cm-1). The PC1-PC2 plot in the analysis of FTIR spectra and the PC2-PC3 plot in the analysis of derivatives showed the best ability to differentiate and identify the extracts. Multivariate PCA and cluster analyses performed well in identifying the type of plant used in the tanning of the studied leathers, especially when applied to the derivatives of FTIR spectra.

Comparison of milk fat globule membrane protein profile among bovine, goat and camel milk based on label free proteomic techniques

Milk fat globule membrane (MFGM) proteins, which are recognized to play a variety of biological roles; nevertheless, their composition and interspecies complexity remain unknown. This study aimed to identify and quantify proteins extracted from MFGM-enriched fractions of bovine, goat and camel milk by a label free proteomics approach. We identified 1579 proteins, substantially increasing the number of MFGM proteins identified for these species. The results of functional analysis showed that the identified MFGM proteins had similar functional annotations, with cellular processes, intracellular anatomical structures and binding being the predominant GO annotations. And most of the proteins were involved in KEGG pathways such as protein processing of ribosomes and endoplasmic reticulum. Subsequently, multiple statistical methods were used to compare the differences in MFGM protein quantification among different species. The results of principal component analysis showed that bovine, goat and camel milk MFGM proteins were significantly different, among which CSN1S1, XDH, LTF and PI4KA played an important role in taxonomic identification. Meanwhile the results of hierarchical clustering showed that the differences (p < 0.05) between bovine and goat milk were smaller than those of camel milk. These new data deepen the understanding of the protein composition of MFGM and its possible physiological roles, providing directions for the production of specific functional milk proteins.

Screening and identification of miRNAs regulating Tbx4/5 genes of Pampus argenteus.

BackgroundSilver pomfret (Pampus argenteus) is one of the most widely distributed and economically important pelagic fish species. However, an unique morphological feature of P. argenteus is the loss of pelvic fins, which can increase the energy requirement during food capture to some extent and is therefore not conducive to artificial culture. Tbx4/5 genes are highly conserved regulatory factors that regulate limb development in vertebrates and are in turn regulated by microRNAs (miRNAs). However, the miRNAs that directly regulate the Tbx4/5 genes in P. argenteus remain to be elucidated.MethodsThe Tbx4/5 genes of P. argenteus were first cloned, and the small RNA transcriptomes were sequenced by high-throughput sequencing during the critical period of the fin development at days 1, 7, and 13 of hatching. The miRNAs regulating the Tbx4/5 genes of P. argenteus were subsequently predicted by bioinformatics analysis, and the related miRNAs were verified in vitro using a dual fluorescence reporter system.ResultsA total of 662 miRNAs were identified, of which 257 were known miRNAs and 405 were novel miRNAs were identified. Compared to day 1, 182 miRNAs were differentially expressed (DE) on day 7, of which 77 and 105 miRNAs were downregulated and upregulated, respectively, while 278 miRNAs were DE on day 13, of which 136 and 142 miRNAs were downregulated and upregulated, respectively. Compared to day 13, four miRNAs were DE on day 7, of which three miRNAs were downregulated and one miRNA was upregulated. The results of hierarchical clustering of the miRNAs revealed that the DE genes were inversely expressed between days 1 and 7, and between days 1 and 13 of larval development, indicating that the larvae were in the peak stage of differentiation. However, the number of DE genes between days 7 and 13 of larval development was relatively small, suggesting the initiation of development. The potential target genes of the DE miRNAs were subsequently predicted, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of target genes were performed. The results suggested that the DE miRNAs were involved in growth, development, and signal transduction pathways, of which the Wnt and Fgfs signaling pathways are known to play important roles in the growth and development of fins. The results of dual fluorescence reporter assays demonstrated that miR-102, miR-301c, and miR-589 had a significant negative regulatory effect on the 3′-UTR of the Tbx4 gene, while miR-187, miR-201, miR-219, and miR-460 had a significant negative regulatory effect on the 3′-UTR of the Tbx5 gene. Altogether, the findings indicated that miRNAs play an important role in regulating the growth and development of pelvic fins in P. argenteus. This study provides a reference for elucidating the interactions between the miRNAs and target genes of P. argenteus in future studies.

Are non-fungible tokens (NFTs) different asset classes? Evidence from quantile connectedness approach

The emerging Non-fungible tokens (NFTs) have received investors’ attention as an alternative asset, whereas the nexus between NFTs and major asset classes remains unsettled and emerges as an issue of unquestionable interest. Using a novel Q-Joint spillover model, we explore the quantile connectedness between NFTs and common asset classes. We reveal that return and volatility cross-asset spillovers are much higher in extreme market conditions than that of normal times. The noticeable directional connectedness and results of hierarchical clustering of directional spillovers hint that NFTs should not be considered as a distinct asset class in extreme market conditions.

Аутекологічні особливості та характеристика еконіш діатомових водоростей (Bacillariophyta Karsten) пониззя р. Ворскла

The species diversity of diatoms in the lower reaches of the Vorskla River, which belong to the territory of the Regional Landscape Park "Nizhnovorsklyansky", is analyzed. The material for the work comes from algological samples collected in the lower reaches of the Vorskla River in the spring and autumn periods. A section of about 7 km of the lower reaches of the Voskla River, a section of the Vorskla Bay and the Kamyanke Reservoir, floodland water bodies, swamps and wetlands were surveyed. Collection of algae samples was carried out from different ecology groups. Samples were processed in live and fixed state using the method of direct microscopy. A total of 288 taxa were identified. An analysis of the autecology of 133 representatives of diatoms in relation to the main environmental factors (temperature, acidity, salinity) has been carried out. The obtained digital data were processed by generally accepted methods of statistical research. Based on Ellenberg scales, phytoindication methods are used to characterize the ecological niche and to clarify the nature of the interaction between the main ecofactors of the noted representatives of &lt;i&gt;Bacillariophyta&lt;/i&gt;. Based on the analysis of the values of the range of variation of ecofactors, the range of the optimum zone for the studied species was established, and according to the tolerance amplitude (BP) they were divided into 4 classes (stenotopic, hemistenotopic, hemieurytopic and eurytopic). It is established that the ecological niche of the studied taxa is characterized by a shift towards a neutral environment in terms of acidity (6.5 ≤ рН ≥ 7.5) and salinity 0–2‰. Groups of stenotopic species to the temperature, salinity and acidity of the environment, which can be sensitive indicators of changes in these environmental factors, have been identified. According to the results of hierarchical clustering, the affinity of the studied indicator species was established. Clusters that unite a complex of species characterized by similar values of ecological amplitude to the leading environmental factors were formed.