Distinct Clusters Research Articles

Transnational cultural networks: soft mechanisms for cultural diversity and frictionless mobility

ABSTRACT Transnational networks in the cultural sector have expanded significantly in Europe over the last three decades. Nevertheless, although this phenomenon is fuelled by the EU’s cultural policy and corresponding funding initiatives, there is a noticeable lacuna in the academic literature on this issue. This article, therefore, examines the emergence and institutionalisation of these networks, shedding light on: 1) the relationship between networks’ evolution and the EU’s cultural policy discourse, and 2) networks and the Europeanisation process. To this end, the study employs a document analysis and uncovers three distinct policy clusters, with a shift from a transnational European identity to one of cultural diversity and a people-centered approach. These outcomes lead to an investigation of whether and how these discursive shifts are interconnected with the development of transnational networks. This conclusively reveals how their implementation is intricately linked not only to the foundational tenets of the EU’s cultural policy – the subsidiarity principle, but also to the broader context of EU policymaking. The article concludes that EU intervention in the cultural sector can be justified as a discursive agent in the evolving European public sphere, with significant implications for professionals, policymakers and scholars in the field.

Are skepticism and moderation dominating attitudes toward AI‐based technologies?

AbstractAI advancements are poised to substantially modify human abilities in the foreseeable future. They include the integration of Brain–Computer Interfaces (BCIs) to augment cognitive functions, the application of gene editing, and the utilization of AI‐powered robotic exoskeletons to enhance physical strength. This study employs a comprehensive analytical framework combining factor analysis, clustering, ANOVA, and logistic regression to investigate public attitudes toward these transformative technologies. Our findings reveal three distinct clusters of public opinion reflecting varying optimism and concern toward AI technologies. Cluster 1 (1574 participants) held a positive view with high excitement while Cluster 2 (1334 participants) showed a balanced stance. Cluster 3 (2199 participants) expressed heightened concern despite some excitement. Notably, regional disparities, particularly between urban and rural participants, emerge as a prominent factor influencing these attitudes (ANOVA, F = 15.2, p < 0.001). Furthermore, logistic regression identifies key influencers of public perception, highlighting the significant roles played by religion and regional factors. The implications of these findings extend beyond understanding public sentiment. They underscore the need for informed policies that promote education and awareness about AI technologies, address ethical concerns, and engage the public in decision‐making processes. As society navigates this transformative technological landscape, a nuanced understanding of public attitudes becomes paramount, guiding ethical regulation, innovation, and public engagement strategies. This study provides valuable insights into the intricate dynamics surrounding AI acceptance and highlights the importance of adapting measures to evolving perceptions and attitudes among the general public.

Classification of asbestos and their nonasbestiform analogues using FTIR and multivariate data analysis

This study presents a possible application of Fourier transform infrared (FTIR) spectrometry and multivariate data analysis, principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA) for classifying asbestos and their nonasbestiform analogues. The objectives of the study are: 1) to classify six regulated asbestos types and 2) to classify between asbestos types and their nonasbestiform analogues. The respirable fraction of six regulated asbestos types and their nonasbestiform analogues were prepared in potassium bromide pellets and collected on polyvinyl chloride membrane filters for FTIR measurement. Both PCA and PLS-DA classified asbestos types and their nonasbestiform analogues on the score plots showed a very distinct clustering of samples between the serpentine (chrysotile) and amphibole groups. The PLS-DA model provided ∼95% correct prediction with a single asbestos type in the sample, although it did not provide all correct predictions for all the challenge samples due to their inherent complexity and the limited sample number. Further studies are necessary for a better prediction level in real samples and standardization of sampling and analysis procedures.

ScRNA-seq reveals dark- and light-induced differentially expressed gene atlases of seedling leaves in Arachis hypogaea L.

Although the regulatory mechanisms of dark and light-induced plant morphogenesis have been broadly investigated, the biological process in peanuts has not been systematically explored on single-cell resolution. Herein, 10 cell clusters were characterized using scRNA-seq-identified marker genes, based on 13 409 and 11 296 single cells from 1-week-old peanut seedling leaves grown under dark and light conditions. 6104 genes and 50 transcription factors (TFs) displayed significant expression patterns in distinct cell clusters, which provided gene resources for profiling dark/light-induced candidate genes. Further pseudo-time trajectory and cell cycle evidence supported that dark repressed the cell division and perturbed normal cell cycle, especially the PORA abundances correlated with 11 TFs highly enriched in mesophyll to restrict the chlorophyllide synthesis. Additionally, light repressed the epidermis cell developmental trajectory extending by inhibiting the growth hormone pathway, and 21 TFs probably contributed to the different genes transcriptional dynamic. Eventually, peanut AHL17 was identified from the profile of differentially expressed TFs, which encoded proteinlocated in the nucleus promoted leaf epidermal cell enlargement when ectopically overexpressed in Arabidopsis through the regulatory phytohormone pathway. Overall, our study presents the different gene atlases in peanut etiolated and green seedlings, providing novel biological insights to elucidate light-induced leaf cell development at the single-cell level.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels the molecular feature of M2 macrophages of head and neck squamous cell carcinoma.

The connection between head and neck squamous cell carcinoma (HNSC) and M2 tumour-associated macrophages is not yet fully understood. We gathered gene expression profiles and clinical data from HNSC patients in the TCGA database. Using Consensus Clustering, we categorized these patients into M2 macrophage-related clusters. We developed a M2 macrophage-related signature (MRS) through statistical analyses. Additionally, we assessed gene expression in HNSC cells using single-cell sequencing data (GSE139324). We identified three distinct M2 macrophage-related clusters in HNSC, each with different prognostic outcomes and immune characteristics. Patients with different MRS profiles exhibited variations in immune infiltration, genetic mutations and prognosis. FCGR2A may play a role in creating an immunosuppressive tumour microenvironment and could potentially serve as a therapeutic target for HNSC. Our study demonstrated that M2 macrophage-related genes significantly impact the development and progression of HNSC. The M2 macrophage-related model offered a more comprehensive assessment of HNSC patient prognosis, genetic mutations and immune features. FCGR2A was implicated in immunosuppressive microenvironments and may hold promise for the development of novel immunotherapeutic strategies for HNSC.

In silico elucidation for the identification of potential phytochemical against ACE-II inhibitors.

The present study aims to investigate the therapeutic potential of phytocompounds derived from Annona reticulata leaves for the treatment of hypertension, utilizing computational methodologies. Gaining a comprehensive understanding of the molecular interactions between neophytadiene and γ-sitosterol holds significant importance in the advancement of innovative therapeutic approaches. This study aims to examine the inhibitory effects of neophytadiene and γ-sitosterol using molecular docking and dynamics simulations. Additionally, we will evaluate their stability and predict their drug-like properties as well as their ADME/toxicity profiles. Neophytadiene and γ-sitosterol have a substantial binding affinity with 1O8A, as shown by the docking study. The stability of the complexes was confirmed through molecular dynamics simulations, while distinct clusters were identified using PCA. These findings suggest the presence of potential stabilizers. The drug-likeness and ADME/toxicity predictions revealed positive characteristics, such as efficient absorption rates, limited distribution volume and non-hazardous profiles. The neophytadiene and γ-sitosterol exhibit potential as hypertension medication options. Computational investigations reveal that these compounds exhibit high affinity for binding, stability and favourable pharmacokinetic properties. The results of this study lay the groundwork for additional experimental verification and highlight the promising prospects of utilizing natural compounds in the field of pharmaceutical research. Target proteins (1O8A) were used to perform molecular docking with representative molecules. Stability, conformational changes and binding energies were assessed through molecular dynamics simulations lasting 100ns. Principal component analysis (PCA) was utilized to analyze molecular dynamics (MD) simulation data, to identify potential compounds that could stabilize the main protease. The safety and pharmacokinetic profiles of the compounds were evaluated through drug-likeness and ADME/toxicity predictions.

Genetic diversity of Ulva lactuca from the intertidal zone in Ulee Lheue beach Aceh, Indonesia

Ulee Lheue Beach in Banda Aceh was developed for tourism. The macroalgae and seaweed species found in this area have been widespread but never exploited. Among these species is the green seaweed, Ulva lactuca, commonly known as sea lettuce. The physiological variations and genetic features of the seaweed can be attributed to the variations in the coastal environment. This study aims to describe the molecular identity of the genetic diversity of U. lactuca from the intertidal zone at Ulee Lheue Beach. The development of molecular biotechnology has enabled identification the gene expression through genomic DNA to PCR amplification. Genetic distance was determined using UPGMA. The results from 600-bp fragments were analyze the genetic diversity. A total of 15 (31%) expression identified as polymorphic (0.500). Heterozygosity (He) and allelic differential (Na) diversity were found of 1.500–3.000. The highest PIC was observed in the rbcL1, with a correlation between subpopulations of 0.459. PCR amplification using the degenerate primer rbcL1 produced fragments ranging from 300 to 460 bp, whereas the expression of UL2 was detected at 448 and 500 bp using the rbcL2 primer. The phylogenetic identify are shown two (2) clusters. The populations of UL1, UL3, and UL4 were found a close relationship. Furthermore, the UL1 and UL2 populations were further divided into distinct clusters but related to the main branch of UL3. Based on this research, the concern of U. lactuca species for industrial and biotechnology destinations, we can describe a suitable method for obtaining he genetic distances between species.

ENHANCING QUALITY OF LIFE IN THE CAMPUS COMMUNITY: THE EFFECTIVENESS OF THE GREEN CAMPUS INITIATIVE

This study seeks to investigate the existing green practices implemented at UniSZA to achieve sustainability and enhance the quality of life for campus society. This study employs quantitative methodologies, specifically utilizing questionnaire survey techniques to gather empirical data. The Pearson Correlation and Cluster Analysis were utilized to determine the correlation among the questions. The result indicates that there is a positive correlation between all questions. Only some questions have weak correlations which correlated to the recognition of the significance of sustainability and the adoption of tangible measures to translate its principles into reality. The cluster analysis successfully sorted the 20 questions related to green campus and perceived quality of life into three distinct clusters: high perceived quality of life, moderate perceived quality of life, and low perceived quality of life. The findings indicated that the UniSZA society expressed a good perceived quality of life regarding their mean score. However, in terms of satisfaction with their campus society, UniSZA still lacks the green campus aspect implementation. Therefore, it is imperative to heighten awareness of the green campus aspect through the collaboration of the entire campus society, emphasizing the importance of green technology in achieving sustainable development.

Variability in phenolic compounds, antioxidant capacity and essential oil profile in different tissues of Ferula assa-foetida L. populations in Iran: an opportunity for industrial products

AbstractPlant responses to multiple environmental conditions in the habitat are characterized by intra-species variety. Ferula assa-foetida L. is a native medical plant in Iran, which has a wide range of therapeutic and pharmacological properties. The objective of the current study was to identify changes in phenolic content, antioxidant capacity and essential oil (EO) composition in different tissues (leaf, flower and fruit) of three populations of F. assa-foetida L. (Sarbisheh, Nehbandan, located in South Khorasan province of Iran). The results showed that total phenolic content (TPC), total flavonoid content (TFC) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity in the leaves of Sarbisheh were higher than in other treatments. The amount of TPC, TFC and DPPH scavenging activity in leaves was higher than in other tissues in the Sarbisheh population, increasing by 31 and 70%, 41 and 20%, 8 and 13%, respectively, over that of flowers and fruits. The main EO compounds of leaves were α-pinene (11.86–13.56%), β-pinene (6.80–7.99%), bornyl acetate (15.80–17.33%). The main EO constitutes of flowers were Z-propenyl-sec-butyldisulfide (10.12–12.4%), eco-fenchyl acetate (10.87–12.4%), myrcisticin (13.43–15.87%) and α-bisabolol (11.23–13.76%). The primary compounds of fruit EO were β-pinene (9.32–11.26%), (Z)-1-propenyl sec butyldisulf (17.54–18.87%) and (E)-1-propenyl sec butyldisulf (28.32–30.02%). The agglomerative hierarchical clustering showed that three distinguished clusters were identified for population, where Nehbandan and Qaen had the higher similarity and Sarbisheh completely differed from the two ones. Different plant tissues contain varying types of chemicals, with leaves exhibiting higher levels of phenolic content and antioxidant capacity. This characteristic can be beneficial for industries such as food and pharmaceuticals.

Distinct genetic clusters in HIV-1 CRF01_AE-infected patients induced variable degrees of CD4+ T-cell loss.

Retroviruses swiftly adapt, employing error-prone enzymes for genetic and phenotypic evolution, optimizing survival strategies, and enhancing virulence levels. HIV-1 CRF01_AE has persistently undergone adaptive selection, and cluster 1 and 2 infections display lower counts and fast loss of CD4+ T cells than other HIV-1 sub-types and CRF01_AE clusters. Its mechanisms are associated with increased CXCR4 tropism due to an envelope structure change favoring a tropism shift from CCR5 to CXCR4, thereby shaping viral phenotype features and impacting pathogenicity. This underscores the significance of consistently monitoring HIV-1 genetic evolution and phenotypic transfer to see whether selection bias across risk groups alters the delicate balance of transmissible versus toxic trade-offs, since virulent strains such as CRF01_AE clusters 1 and 2 could seriously compromise the efficacy of antiviral treatment. Only through such early warning and diagnostic services can precise antiviral treatments be administered to those infected with more virulent HIV-1 strains.

Describing patterns of familial cancer risk in subfertile men using population pedigree data.

Can we simultaneously assess risk for multiple cancers to identify familial multicancer patterns in families of azoospermic and severely oligozoospermic men? Distinct familial cancer patterns were observed in the azoospermia and severe oligozoospermia cohorts, suggesting heterogeneity in familial cancer risk by both type of subfertility and within subfertility type. Subfertile men and their relatives show increased risk for certain cancers including testicular, thyroid, and pediatric. A retrospective cohort of subfertile men (N = 786) was identified and matched to fertile population controls (N = 5674). Family members out to third-degree relatives were identified for both subfertile men and fertile population controls (N = 337754). The study period was 1966-2017. Individuals were censored at death or loss to follow-up, loss to follow-up occurred if they left Utah during the study period. Azoospermic (0 × 106/mL) and severely oligozoospermic (<1.5 × 106/mL) men were identified in the Subfertility Health and Assisted Reproduction and the Environment cohort (SHARE). Subfertile men were age- and sex-matched 5:1 to fertile population controls and family members out to third-degree relatives were identified using the Utah Population Database (UPDB). Cancer diagnoses were identified through the Utah Cancer Registry. Families containing ≥10 members with ≥1 year of follow-up 1966-2017 were included (azoospermic: N = 426 families, 21 361 individuals; oligozoospermic: N = 360 families, 18818 individuals). Unsupervised clustering based on standardized incidence ratios for 34 cancer phenotypes in the families was used to identify familial multicancer patterns; azoospermia and severe oligospermia families were assessed separately. Compared to control families, significant increases in cancer risks were observed in the azoospermia cohort for five cancer types: bone and joint cancers hazard ratio (HR) = 2.56 (95% CI = 1.48-4.42), soft tissue cancers HR = 1.56 (95% CI = 1.01-2.39), uterine cancers HR = 1.27 (95% CI = 1.03-1.56), Hodgkin lymphomas HR = 1.60 (95% CI = 1.07-2.39), and thyroid cancer HR = 1.54 (95% CI = 1.21-1.97). Among severe oligozoospermia families, increased risk was seen for three cancer types: colon cancer HR = 1.16 (95% CI = 1.01-1.32), bone and joint cancers HR = 2.43 (95% CI = 1.30-4.54), and testis cancer HR = 2.34 (95% CI = 1.60-3.42) along with a significant decrease in esophageal cancer risk HR = 0.39 (95% CI = 0.16-0.97). Thirteen clusters of familial multicancer patterns were identified in families of azoospermic men, 66% of families in the azoospermia cohort showed population-level cancer risks, however, the remaining 12 clusters showed elevated risk for 2-7 cancer types. Several of the clusters with elevated cancer risks also showed increased odds of cancer diagnoses at young ages with six clusters showing increased odds of adolescent and young adult (AYA) diagnosis [odds ratio (OR) = 1.96-2.88] and two clusters showing increased odds of pediatric cancer diagnosis (OR = 3.64-12.63). Within the severe oligozoospermia cohort, 12 distinct familial multicancer clusters were identified. All 12 clusters showed elevated risk for 1-3 cancer types. An increase in odds of cancer diagnoses at young ages was also seen in five of the severe oligozoospermia familial multicancer clusters, three clusters showed increased odds of AYA diagnosis (OR = 2.19-2.78) with an additional two clusters showing increased odds of a pediatric diagnosis (OR = 3.84-9.32). Although this study has many strengths, including population data for family structure, cancer diagnoses and subfertility, there are limitations. First, semen measures are not available for the sample of fertile men. Second, there is no information on medical comorbidities or lifestyle risk factors such as smoking status, BMI, or environmental exposures. Third, all of the subfertile men included in this study were seen at a fertility clinic for evaluation. These men were therefore a subset of the overall population experiencing fertility problems and likely represent those with the socioeconomic means for evaluation by a physician. This analysis leveraged unique population-level data resources, SHARE and the UPDB, to describe novel multicancer clusters among the families of azoospermic and severely oligozoospermic men. Distinct overall multicancer risk and familial multicancer patterns were observed in the azoospermia and severe oligozoospermia cohorts, suggesting heterogeneity in cancer risk by type of subfertility and within subfertility type. Describing families with similar cancer risk patterns provides a new avenue to increase homogeneity for focused gene discovery and environmental risk factor studies. Such discoveries will lead to more accurate risk predictions and improved counseling for patients and their families. This work was funded by GEMS: Genomic approach to connecting Elevated germline Mutation rates with male infertility and Somatic health (Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD): R01 HD106112). The authors have no conflicts of interest relevant to this work. N/A.

NOMA-based retrodirective frequency diverse array for multi-user communication

Frequency Diverse Array (FDA) and retrodirective array have found applications in wireless communication networks, offering cost-effective and efficient beamforming capabilities. In this paper, a novel concept of the retrodirective frequency diverse array (RFDA) is introduced, which empowers each array element to handle multiple communication channels, thereby enabling support for multiple users. This is made possible by utilizing the non-orthogonal multiple access (NOMA) scheme. The antenna elements are organized in a uniform circular array configuration. The system supports the multitude of users by assigning them into distinct clusters based on the serving antenna and then allocating transmit power to each user based on their respective channel conditions and distances from the base station. Consequently, efficient schemes for user clustering and power allocation tailored to the NOMA-RFDA system are developed. As a result, multi-user connectivity is achieved while maintaining low design costs. The simulation outcomes affirm that the proposed system attains superior performance in terms of both data rate and energy efficiency when compared to similar reference schemes.

Exploring behavioural patterns and their relationships with social annotation outcomes

AbstractBackgroundSocial annotation has emerged as a promising educational technology that fosters collaborative reading and discussion of digital resources among learners. While the positive impact of social annotation on students' learning process and performance is widely acknowledged, students' behavioural patterns in social annotation are underexplored.ObjectivesThis study investigated patterns in students' use of annotation and response behaviours in social annotation activities. We also explored how students' performance in the behavioural, cognitive, emotional, and social dimensions varied based on their behavioural patterns.MethodsWe recruited 93 undergraduates who were enrolled in an elective course at a large North American University. Students were tasked with collaboratively annotating the class readings uploaded to Perusall, a social annotation platform, over 7 weeks. We used metaclustering to determine the optimal number of clusters pertaining to student behaviours. We compared the differences among clusters across multiple performance dimensions.Results and ConclusionsTwo distinct clusters were identified and defined as initiators and responders. We found that responders had significantly longer active reading time and exhibited greater social annotation effort compared to initiators. However, initiators received more peer acknowledgement, as evidenced by higher upvotes. No significant difference was found in cognitive insight between initiators and responders, but responders demonstrated significantly higher cognitive discrepancy. Additionally, there were no significant differences in positive and negative tones between initiators and responders; however, responders displayed higher levels of prosocial behaviours than initiators. This study has significant practical implications regarding promoting students' collaborative learning experience in social annotation.

Effect of cinnamon powder on quality attributes and off-flavor in fried chicken drumsticks made from long-term thawed Korean native chicken

The effect of cinnamon powder on the quality and mitigation of off-flavor in fried chicken drumsticks made from long-term thawed Korean native chicken (Woorimatdag No. 1, WRMD1) was investigated. The WRMD1 drumsticks were categorized into 5 groups: conventional thawing (16 h, CT), long-term thawing (48 h, LT), cinnamon powder added into 'LT' as marinade (0.03%, CM) or incorporated into the batter (1.35%, CB), and long-term thawing with cinnamon powder incorporated both in the marinade and batter (0.03% + 1.35%, CMB). The crude fat content was significantly higher in the CT and CMB than that of the CB. The CM, CB, and CMB showed significantly lower levels of 2-thiobarbituric acid reactive substance compared with the CT and LT. The predominant fatty acids in all treatments were C18:1n9, C18:2n6, and C16:0. The LT displayed lower total unsaturated fatty acid content than the CT (P < 0.05). The CM effectively decreased lipid oxidative volatiles, such as 1-octanol, 1-octen-3-ol, and 2-octen-1-ol, (E), in the LT (P < 0.05). Both the CM and CB showed an inclination to increase specific pyrazines associated with pleasant notes compared with the LT, and showed higher levels of pyrazines, such as pyrazine, 2-ethyl-6-methyl-, and pyrazine, 3-ethyl-2,5-dimethyl-, than those of the CMB (P < 0.05). The CM contained higher levels of 2,3-butanedione when compared with the other groups (P < 0.05). Multivariate analysis demonstrated that cinnamon had an effect in discriminating the treatment groups with cinnamon addition from both the CT and LT, whereas the CM, CB, and CMB formed distinct clusters. The CM and CMB received significantly higher aroma scores from panelists in comparison to the other groups. These findings suggest that the CM (0.03% cinnamon powder) can be used to enhance the aroma in fried WRMD1 drumsticks by reducing or masking the off-flavor volatiles associated with long-term thawing.

Radiologic Classification of Hippocampal Sclerosis in Epilepsy.

Temporal lobe epilepsy is a common form of epilepsy that is often associated with hippocampal sclerosis (HS). Although HS is commonly considered a binary assessment in radiologic evaluation, it is known that histopathologic changes occur in distinct clusters. Some subtypes of HS only affect certain subfields, resulting in minimal changes to the overall volume of the hippocampus. This is likely a major reason why whole hippocampal volumetrics have underperformed versus expert readers in the diagnosis of HS. With recent advancements in MRI technology, it is now possible to characterize the substructure of the hippocampus more accurately. However, this is not consistently addressed in radiographic evaluations. The histologic subtype of HS is critical for prognosis and treatment decision-making, necessitating improved radiologic classification of HS. The International League Against Epilepsy (ILAE) has issued a consensus classification scheme for subtyping HS histopathologic changes. This review aims to explore how the ILAE subtypes of HS correlate with radiographic findings, introduce a grading system that integrates radiologic and pathologic reporting in HS, and outline an approach to detecting HS subtypes by using MRI. This framework will not only benefit current clinical evaluations, but also enhance future studies involving high-resolution MRI in temporal lobe epilepsy.

Single-cell RNA sequencing of anaplastic ependymoma and H3K27M-mutant diffuse midline glioma.

Anaplastic ependymoma and H3K27M-mutant diffuse midline glioma are two common subtypes of brain tumors with poor long-term prognosis. The present study analyzed and compared the differences in cell types between two tumors by single-cell RNA sequencing (scRNA-seq) technology. ScRNA-seq was performed to profile cells from cancer tissue from anaplastic ependymoma patient and H3K27M-mutant diffuse midline glioma patient. Cell clustering, marker gene identification, cell type annotation, copy number variation analysis and function analysis of differentially expressed genes were then performed. A total of 11,219 cells were obtained from anaplastic ependymoma and H3K27M mutant diffuse midline glioma, and these cells categorized into 12 distinct clusters. Each cell cluster could be characterized with specific cell markers to indicate cellular heterogeneity. Five cell types were annotated in each sample, including astrocyte, oligodendrocytes, microglial cell, neural progenitor cell and immune cell. The cluster types and proportion of cell types were not consistent between the two brain tumors. Functional analyses suggest that these cell clusters are involved in tumor-associated pathways, with slight differences in the cells of origin between the two tumors. In addition, cell communication analysis showed that the NRG3-ERBB4 pair is a key Ligand-receptor pair for anaplastic ependymoma, while in H3K27M-mutant diffuse midline glioma it is the PTN-PTPRZ1 pair that establishes contact with other cells. There was intratumor heterogeneity in anaplastic ependymoma and H3K27M mutant diffuse midline glioma, and that the subtype differences may be due to differences in the origin of the cells.

Deep clustering using 3D attention convolutional autoencoder for hyperspectral image analysis

Deep clustering has been widely applicated in various fields, including natural image and language processing. However, when it is applied to hyperspectral image (HSI) processing, it encounters challenges due to high dimensionality of HSI and complex spatial-spectral characteristics. This study introduces a kind of deep clustering model specifically tailed for HSI analysis. To address the high dimensionality issue, redundant dimension of HSI is firstly eliminated by combining principal component analysis (PCA) with t-distributed stochastic neighbor embedding (t-SNE). The reduced dataset is then input into a three-dimensional attention convolutional autoencoder (3D-ACAE) to extract essential spatial-spectral features. The 3D-ACAE uses spatial-spectral attention mechanism to enhance captured features. Finally, these enhanced features pass through an embedding layer to create a compact data-representation, and the compact data-representation is divided into distinct clusters by clustering layer. Experimental results on three publicly available datasets validate the superiority of the proposed model for HSI analysis.

Non-Iterative Cluster Routing: Analysis and Implementation Strategies

In conventional routing, a capsule network employs routing algorithms for bidirectional information flow between layers through iterative processes. In contrast, the cluster routingtechnique utilizes a non-iterative process and can outperform state-of-the-art models with fewer parameters, while preserving the part–whole relationship and demonstrating robust generalization to novel viewpoints. This paper aims to further analyze and clarify this concept, providing insights that allow users to implement the cluster routing technique efficiently. Additionally, we expand the technique and propose variations based on the routing principle of achieving consensus among votes in distinct clusters. In some cases, these variations have the potential to enhance and boost the cluster routing performance while utilizing similar memory and computing resources.

Unravelling genetic diversity and population structure of Sarocladium oryzae causing sheath rot disease in rice using hyper-variable SSR markers

Rice sheath rot, caused by the fungus Sarocladium oryzae, poses a major threat to rice crops. This pathogen exhibits substantial variability in the field, yet limited information on its genetic diversity and population structure hampers the development of effective disease management strategies. In this study, hyper-variable Simple Sequence Repeats (HV-SSR) markers were employed for the first time to assess the genetic diversity of Sarocladium oryzae, collected from various rice cultivating regions in Odisha, Eastern India. The isolates were identified using both morphological and molecular criteria. A pathogenicity test was performed for all the isolates. A total of 2558 HV-SSRs with more than 10 repeat units were identified in the genome of S. oryzae. The most prevalent types of repeats were dinucleotides (26.8%), followed by trinucleotides (22.8%), tetranucleotides (8.67%), pentanucleotides (2.83%), and hexanucleotides (1.83%). A total of 150 HV-SSR primers were designed and 24 were randomly selected and validated, of which ten of them demonstrating polymorphism in the 87 isolates. These ten polymorphic hyper-variable SSR markers were subsequently used to assess the genetic diversity and population structure of Sarocladium oryzae isolates. The markers revealed a total of 32 alleles, with the number of alleles per locus varying from 2 to 4. Gene diversity ranged from 0.409 to 0.641, while the major allele frequency varied from 0.448 to 0.736. The Polymorphic Information Content (PIC) values ranged from 0.325 to 0.569, with an average of 0.457. Cluster analysis categorized the sheath rot isolates into three distinct major clusters. Principal coordinate analysis and structure analysis identified two genetic clusters within S. oryzae isolates. Analysis of molecular variance indicated that there was more genetic variation within populations (55%) and less variation among populations (45%). The findings of this study provide insights into the genetic diversity of S. oryzae in the rice-cultivating regions of Odisha, which can be used to develop more effective disease management strategies.

Employee Technostress: Analyzing the Influence of Sociodemographic Characteristics on Techno-Stressors and Their Consequences

Technostress has garnered increasing interest in recent times due to the pervasive role of technology and its impact on well-being and overall quality of life. Understanding and addressing technostress is crucial to promoting healthier relationships with technology, mitigating its negative effects, and developing strategies to manage the challenges it poses. This article presents a set of sociodemographic characteristics, explores their relationships with different dimensions of technostress (i.e., techno-stressors), and analyzes the consequences of these techno-stressors. The aim of this research is threefold: first, we examine how the sociodemographic characteristics of gender, age, living arrangement, education level, work experience, tenure, organization size, and organization type relate to techno-stressors, namely techno-overload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty. Second, we identify technostress profiles based on their levels of technostress across these five dimensions. Third, we analyze the impact of the selected sociodemographic variables in predicting cluster membership and examine differences in role overload, role conflict, life satisfaction, and work–family conflict across the identified clusters. Data were collected cross-sectionally from a convenience sample of 1187 Chilean workers and analyzed using R Studio for Windows. Descriptive statistics, cluster analysis, and multinomial logistic regression were performed. Our results show that certain sociodemographic variables have a significant impact on specific dimensions of technostress. We also identified three distinct clusters based on their technostress levels (high, moderate, and low). Cluster 1 emerged as the profile with the lowest levels across all dimensions, cluster 2 displayed moderate levels, and cluster 3 exhibited the highest levels. Multinomial logistic regression also revealed that living alone, education level, tenure, and organization size were all significant predictors of technostress cluster membership. There were also significant differences among the three clusters in terms of the consequences of technostress. Specifically, individuals in the high technostress cluster reported higher levels of role overload and role conflict, lower life satisfaction, and greater work–family conflict. Future research avenues and implications for mental health and well-being are discussed at the end of the article.