Cluster Variance Research Articles

Enhancing Fuzzy C-Means Clustering with a Novel Standard Deviation Weighted Distance Measure

ان الهدف الرئيس من هذه الورقة هو تقديم منهج جديد لمعالجة خوارزمية عنقدة المتوسطات الضبابيةFCM التي تُعد من اهم واشهر الخوارزميات التي عالجت ظاهرة عدم اليقين في تشكيل العناقيدعلى وفق نسب التداخل، ان من اهم المشكلات التي تواجه هذه الخوارزمية هو اعتمادها بشكل اساسي على مقياس المسافة الاقليدية وبطبيعة الحال هذا المقياس يجعل العناقيد المشكلة تاخذ الشكل الكروي الذي يكون غير قادر على احتواء الحالات المعقدة او المتداخلة، لذا حاولنا من خلال هذه الورقة اقتراح مقياس جديد للمسافة حيث تمكنا من اشتقاق صيغة لتباين العنقود الضبابي ليدخل كوزن على صيغة المسافة الاقليدية علاوة على ذلك تم معالجة حساب مصفوفة التقسيمات من خلال استعمال خوارزمية K-Means وخلق بيئية هجينة بين الخوارزمية الضبابية والخوارزمية الحادة، وللتحقق مما تم عرضه تم استعمال المحاكاة التجريبية ومن ثم تطبيقها على الواقع باستعمال البيانات البيئية للفحص الفيزيائي والكيميائي لمحطات فحص المياه في محافظة البصرة، وقد اثبتت النتائج التجريبية ان مقياس المسافة المقترح (WED) كانت له الافضلية على تحسين عمل خوارزمية HFCM من خلال معيار (Obj_Fun, Iteration, Min_optimization, Good fit clustering and overlap) عندما (k = 2,3)، وبموجب نتائج المحاكاة تم اختيار c = 2 وتشكيل العناقيد للبيانات الحقيقية وتمكنت من ايجاد افضل دالة هدف (23.93، 22.44، 18.83) عند درجات التضبيب (1.2، 2، 2.8) بينما حسب درجة التضبيب (m = 3.6) كانت دالة الهدف لمقاس (ED) هي الاقل ولكن كانت المعايير ( Iter. =2 ، Min_optimization = 0، ) تؤكد على ان (WED) هي الافضل.

Assessing aquatic toxicity assessment via a clustered variance model

Assessing aquatic toxicity assessment via a clustered variance model

Surgical control time estimation variability: Implications for medical systems and the future integration of AI and ML models

BackgroundAccurate estimation of surgical procedure times, crucial for optimizing healthcare access, patient outcomes, and cost-effectiveness, is essential for operating room efficiency. Surgical control time (SCT) is a preoperative estimate by surgeons representing their predicted time to complete the surgery, spanning from completion of anesthesia induction to surgical site closure. MethodsIn this within-subjects, longitudinal study, we examined the differences between predicted surgical control times versus actual SCTs and determined variability by surgical specialty. We included cases regardless of classification (i.e., outpatient or inpatient), type of surgery (i.e., elective, urgent, or emergent), or level of complexity (i.e., major or minor). We ran Shapiro–Wilk tests to assess the normality of the difference in actual versus predicted surgical control times (dSCT) by surgical specialty. We used a generalized linear model (GLM) with robust clustered variance and pairwise comparisons of surgical specialties (with Bonferroni adjustment for family-wise error rate) to assess differences in the prediction accuracy of SCTs by specialty. ResultsWe analyzed 14,438 surgical cases performed by 168 surgeons across 13 specialties from January 2019 to January 2023. 11 of 13 specialties had higher actual than predicted times, suggesting an overall pattern of underestimating SCTs. On average, surgeries took 12.3 % longer than predicted, with surgeons underestimating SCTs by an average of 10.4 min. SCTs comprised 78 % of the total operative time. The four specialties with the largest underestimations of SCTs were neurosurgery (27.04 min), orthopedics (22.75 min), urology (19.4 min, and plastic surgery (18.67 min), while two specialties exhibited overestimations, namely ear nose and throat (11.14 min) and pediatrics (–3.21 min). GLM results and pairwise comparisons showed that surgeons significantly differed in their SCT prediction by surgical specialty. ConclusionsOur findings showed significant differences across surgical specialties in the accuracy of predicting surgical control times. These results have implications for integrating evolving technologies such as artificial intelligence and machine learning models to assist surgical administrators in accurately predicting surgical case durations and optimizing resource allocation.

Geographic Variation of Antidementia and Antipsychotic Medication Use Among US Nursing Home Residents With Dementia.

Several antidementia medications have been approved for symptomatic treatment of cognitive and functional impairment due to Alzheimer disease. Antipsychotics are often prescribed off-label for behavioral symptoms. The aim of this study was to describe the basis for regional variation in antidementia and antipsychotic medication use. US nursing homes (n=9735), hospital referral regions (HRR; n=289). Long-stay residents with dementia (n=273,004). Using 2018 Minimum Data Set 3.0 linked to Medicare data, facility information, and Dartmouth Atlas files, we calculated prevalence of use and separate multilevel logistic models [outcomes: memantine, cholinesterase inhibitor (ChEI), antipsychotic use] estimated adjusted odds ratios (aOR) and 95% CIs for resident, facility, and HRR characteristics. We then fit a series of cross-classified multilevel logistic models to estimate the proportional change in cluster variance (PCV). Overall, 20.9% used antipsychotics, 16.1% used memantine, and 23.3% used ChEIs. For antipsychotics, facility factors [eg, use of physical restraints (aOR: 1.08; 95% CI: 1.05-1.11) or poor staffing ratings (aOR: 1.10; 95% CI: 1.06-1.14)] were associated with more antipsychotic use. Nursing homes in HRRs with the highest health care utilization had greater antidementia drug use (aOR memantine: 1.68; 95% CI: 1.44-1.96). Resident/facility factors accounted for much regional variation in antipsychotics (PCV STATE : 27.80%; PCV HRR : 39.54%). For antidementia medications, HRR-level factors accounted for most regional variation (memantine PCV STATE : 37.44%; ChEI PCV STATE : 39.02%). Regional variations exist in antipsychotic and antidementia medication use among nursing home residents with dementia suggesting the need for evidence-based protocols to guide the use of these medications.

Catchment characteristics dominate the hydrological behavior of closed lakes across the Tibetan Plateau

Naturally closed lakes located on the Tibetan Plateau provide a more authentic depiction of climate change and have undergone significant but dissimilar changes over the past four decades. Although previous research has concentrated on closed lake changes at regional and continental scales, the dominant factors related to closed lakes at the catchment scale remain elusive. This study employed the hierarchical minimum variance clustering method, six representative topographies and land surface descriptors to classify the catchments of 322 closed lakes (>1 km2) across the Tibetan Plateau from 1981 to 2020. To better understand the factors driving the changes in catchment-scale lake areas, we assigned two distinct attribute classes to the resulting classifications: hydro-meteorological characteristics and geographical environmental aspects. Our analysis revealed that five clusters are geographically coherent and exhibit distinct hydro-meteorological characteristics. By assessing decadal-scale changes in catchment characteristics over time for lakes in five clusters with area change of greater than ± 50 %, we found that the most significant driver of catchment changes from 1981 to 2000 was a continuous decline in snowmelt, followed by a substantial increase in precipitation, temperature, and glacial meltwater from 2000 to 2020. Regarding trends and magnitude, different lake area responses may occur in areas with similar physical geography and climatology (i.e., the same cluster). During the past four decades, only 3 % of lakes (19) shrank in area and decreased in change rate. Also, intense evaporation corresponds to a period of decrease in lake area. Despite the variation in lake area change among clusters, precipitation dominates the inconsistent increase in lake area under similar physical geography and climatology, with some exceptions strongly related to groundwater and melted permafrost recharge. Our findings have laid the groundwork for understanding this region's catchment characteristics and the resulting complex hydrological behavior.

Abstract 3451: Association of multi-level factors with cervical cancer screening in healthcare settings

Abstract Background- Few studies have examined the independent contribution of patient, clinician and system factors associated with completing cervical cancer screening. Methods- Using a large retrospective cohort created from electronic health record data, we examined the association of patient- and provider-level characteristics with cervical cancer screening among unknown- and average-risk, screen eligible women who entered the cohort at a primary care visit in 1 of 3 U.S. healthcare systems (Kaiser Permanente Washington, Parkland Health, Mass General Brigham) between 2010-2019. The outcome was time, in days, from cohort entry to screening. We fit piecewise exponential (PWE) survival models, nesting patients hierarchically within provider seen and the facility at the time of their analytic cohort entry visit. We excluded patients whose visit was with providers at their non-principal facility (principal defined as the facility that they saw most patients in 2010). The analysis was stratified by healthcare setting. Results- Median time from cohort entry until screening varied across settings (202-1063 days) and across providers and clinics (between cluster variance= 0.11-0.77, 0.06-1.18, respectively). Many patient-level characteristics (age, cohort entry year, race/ethnicity, number of comorbidities, smoking status, primary care visit within 12 months before cohort entry, and insurance), some of the provider-level characteristics (provider specialty and sex) and social determinants of health measures (Local Isolation Score Hispanic) were associated with time to screening. In the adjusted PWE models, for all three settings: a) Younger patients (21-49 years old) (vs older patients (50-65 years)); b) Hispanic and Non-Hispanic Asian/Pacific Islander (vs Non-Hispanic White); and c) patients seeing ob-gyn providers (vs family medicine providers) had shorter time to screening. Patients with a) higher (vs lower) comorbidity burden, and b) Medicare insurance (vs Commercial) were less likely to receive screened. Conclusion- By identifying characteristics at multiple levels that are associated with the outcome, our results help us identify targets for interventions to improve and increase timely cervical cancer screening in healthcare settings. Citation Format: Rutu Rathod, Ellen Hu, Sandi Pruitt, Jasmin A. Tiro, Jessica Chubak, Jennifer Haas, Steven J. Atlas, Gina Kruse, Amy E. Hughes. Association of multi-level factors with cervical cancer screening in healthcare settings [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 3451.

Exploring excitement counterbalanced by concerns towards AI technology using a descriptive-prescriptive data processing method

Given the current pace of technological advancement and its pervasive impact on society, understanding public sentiment is essential. The usage of AI in social media, facial recognition, and driverless cars has been scrutinized using the data collected by a complex survey. To extract insights from data, a descriptive-prescriptive hybrid data processing method is proposed. It includes graphical visualization, cross-tabulation to identify patterns and correlations, clustering using K-means, principal component analysis (PCA) enabling 3D cluster representation, analysis of variance (ANOVA) of clusters, and forecasting potential leveraged by Random Forest to predict clusters. Three well-separated clusters with a silhouette score of 0.828 provide the profile of the respondents. The affiliation of a respondent to a particular cluster is assessed by an F1 score of 0.99 for the test set and 0.98 for the out-of-sample set. With over 5000 respondents answering over 120 questions, the dataset reveals interesting opinions and concerns regarding AI technologies that have to be handled to facilitate AI acceptance and adoption. Its findings have the potential to shape meaningful dialog and policy, ensuring that the evolution of technology aligns with the values and needs of the people.

Deep Adaptive Graph Clustering via von Mises-Fisher Distributions

Graph clustering has been a hot research topic and is widely used in many fields, such as community detection in social networks. Lots of works combining auto-encoder and graph neural networks have been applied to clustering tasks by utilizing node attributes and graph structure. These works usually assumed the inherent parameters (i.e., size and variance) of different clusters in the latent embedding space are homogeneous, and hence the assigned probability is monotonous over the Euclidean distance between node embeddings and centroids. Unfortunately, this assumption usually does not hold since the size and concentration of different clusters can be quite different, which limits the clustering accuracy. In addition, the node embeddings in deep graph clustering methods are usually L2 normalized so that it lies on the surface of a unit hyper-sphere. To solve this problem, we proposed D eep A daptive G raph C lustering via von Mises-Fisher distributions, namely DAGC. DAGC assumes the node embeddings H can be drawn from a von Mises-Fisher distribution and each cluster k is associated with cluster inherent parameters ρ k which includes cluster center μ and cluster cohesion degree κ. Then we adopt an EM-like approach (i.e., 𝒫( H | ρ ) and 𝒫( ρ | H ), respectively) to learn the embedding and cluster inherent parameters alternately. Specifically, with the node embeddings, we proposed to update the cluster centers in an attraction-repulsion manner to make the cluster centers more separable. And given the cluster inherent parameters, a likelihood-based loss is proposed to make node embeddings more concentrated around cluster centers. Thus, DAGC can simultaneously improve the intra-cluster compactness and inter-cluster heterogeneity. Finally, extensive experiments conducted on four benchmark datasets have demonstrated that the proposed DAGC consistently outperforms the state-of-the-art methods, especially on imbalanced datasets.

A Gait Imagery-Based Brain-Computer Interface with Visual feedback for Spinal Cord Injury Rehabilitation on Lokomat.

Motor Imagery (MI)-based Brain-Computer Interfaces (BCIs) have been proposed for the rehabilitation of people with disabilities, being a big challenge their successful application to restore motor functions in individuals with Spinal Cord Injury (SCI). This work proposes an Electroencephalography (EEG) gait imagery-based BCI to promote motor recovery on the Lokomat platform, in order to allow a clinical intervention by acting simultaneously on both central and peripheral nervous mechanisms. As a novelty, our BCI system accurately discriminates gait imagery tasks during walking and further provides a multi-channel EEG-based Visual Neurofeedback (VNFB) linked to μ (8-12 Hz) and β (15-20 Hz) rhythms around Cz. VNFB is carried out through a cluster analysis strategy-based Euclidean distance, where the weighted mean MI feature vector is used as a reference to teach individuals with SCI to modulate their cortical rhythms. The developed BCI reached an average classification accuracy of 74.4%. In addition, feature analysis demonstrated a reduction in cluster variance after several sessions, whereas metrics associated with self-modulation indicated a greater distance between both classes: passive walking with gait MI and passive walking without MI. The results suggest that intervention with a gait MI-based BCI with VNFB may allow the individuals to appropriately modulate their rhythms of interest around Cz. This work contributes to the development of advanced systems for gait rehabilitation by integrating Machine Learning and neurofeedback techniques, to restore lower-limb functions of SCI individuals.

Quantitative and qualitative yield in sweet maize hybrids.

Today, sweet corn is considered an important vegetable due to its high sugar content and low starch content. Cluster analysis and variance analysis showed that hybrids had variations in yield indices. GB, DE and GS hybrids had similar performance on indices. SE hybrid that has significant performance on zeaxanthin. Biplot showed that fructose, glucose, sucrose and potassium had stability value on hybrids. All the hybrids had the best performance on fructose, glucose, sucrose and potassium factors. Factor biplot positively correlated with yield indices, including calcium, iron, zinc, magnesium, α-Carotene, 9Z-β-Carotene, phosphorus, and β-carotene. On the other hand, there is a positive correlation with fructose, glucose, potassium, lutein, sucrose, β-Cryptoxanthin, and zeaxanthin. So, to evaluate or increase lutein and zeaxanthin, the other parameters like sugar content (fructose, glucose, and sucrose) are important factors and have an effect together. Factor analysis and biplot showed that ME hybrid had a maximum performance on the first factor of yield indices. Also, the second factor of yield indices had a maxi-mum effect on NO hybrids. SE hybrids had maximum performance in zeaxanthin and GS hybrid had maximum performance in zinc, phosphorus, and iron. The dry matter had stability on DB hybrid.

Community-based directly observed therapy is effective and results in better treatment outcomes for patients with multi-drug resistant tuberculosis in Uganda

BackgroundHealth facility-based directly observed therapy (HF DOT) is the main strategy for the management of patients with drug-resistant tuberculosis (DR TB) in Uganda, however, this still yields sub-optimal treatment outcomes. We set out to assess the effectiveness of community-based directly observed therapy (CB DOT) for the treatment of DR TB in Uganda.MethodsUsing a previously developed patient-centered model for CB DOT, we assigned community health workers (CHWs) as primary caregivers to patients diagnosed with DR TB. CHWs administered daily DOT to patients in their homes. Once a month, patients received travel vouchers to attend clinic visits for treatment monitoring. We assessed the effectiveness of this model using a quasi-experimental pre and post-study. From December 2020 to March 2022, we enrolled adult DR-TB patients on the CB DOT model. We collected retrospective data from patients who had received care using the HF DOT model during the year before the study started. The adjusted effect of CB DOT versus HF DOT on DR TB treatment success was estimated using modified Poisson regression model with robust cluster variance estimator.ResultsWe analyzed data from 264 DR TB patients (152 HF DOT, 112 CB DOT). The majority were males (67.8%) with a median age of 36 years (IQR 29 to 44 years). Baseline characteristics were similar across the comparison groups, except for educational level, regimen type, and organizational unit with age being borderline. The treatment success rate in the CB DOT group was 12% higher than that in the HF DOT (adjusted prevalence ratio (aPR)= 1.12 [95%CI 1.01, 1.24], P-value=0.03). Males were less likely to achieve treatment success compared to their female counterparts (aPR=0.87 [95% CI 0.78, 0.98], P-value=0.02). A total of 126 (47.7%) of 264 patients reported at least one adverse event. The HF DOT group had a higher proportion of patients with at least one adverse event compared to the CB DOT group (90/152 [59.2%] versus 36/112 [32.1], P-value<0.01). The model was acceptable among patients (93.6%) and health workers (94.1%).ConclusionsCB DOT for DR-TB care is effective and results in better treatment outcomes than HF DOT. The cost-effectiveness of this model of care should be further evaluated.

Identification of Public Library Visitor Profiles using K-means Algorithm based on The Cluster Validity Index

The existence of a public library in the Gampingan village has a positive impact, such as increasing the literacy culture of the village community. However, the library collection is not sufficient for the needs of visitors. Therefore, it is necessary to add library collections to fulfill the requirement. One of the solutions is mapping the library needs of visitors. The mapping can be done by identifying visitor profiles by grouping visitors based on the criteria of age, gender, type of visitor, and category of book library. One of the methods that can be used in the process of grouping visitors based on criteria is to use the K-Means Clustering method. Determining the number of K cluster centers at K-Means Clustering method that are not appropriate will give bad results, it is necessary to test the number of K cluster centers using the Cluster Validity index by measuring the clusters with cluster variance, within-cluster variance, and between-cluster variance. From the grouping process using K-Means Clustering with Cluster Validity index, we get 3 clusters of visitor profiles with a cluster variance value of less than 0.1. This shows that this method was able to identify the visitor profiles with high grouping accuracy values.

Implementasi metode Ward clustering untuk klasterisasi penduduk yang memiliki jaminan kesehatan pada kabupaten dan kota di Provinsi Maluku

The Health Insurance Program is a protection guarantee for comprehensive health services which includes promotive, preventive and curative and rehabilitative services provided in stages for the community/participants whose contributions are paid by the government. The health insurance program in Maluku Province from year to year has been getting better. However, there are still disparities or gaps between regions in Maluku Province, so it is necessary to study the clusterization of the population who have health insurance in districts/cities in Maluku Province. Ward clustering method is a method that aims to get a cluster that has the smallest possible internal cluster variance. The purpose of this study is to obtain a cluster of residents who have health insurance in districts/cities in Maluku Province. The data used in this study were sourced from Maluku publications in figures at the Maluku Province BPS. This study obtained the results that there are 3 clusters in the cluster of residents who have health insurance in districts/cities in Maluku Province with details of cluster 1 consisting of Tanimbar Islands Regency, Central Maluku, Buru, West Seram, East Seram and South Buru District. Cluster 2 consists of Southeast Maluku Regency, Aru Islands, Southwest Maluku and Tual City. Furthermore, Cluster 3 consists of Ambon City.&#x0D; Keywords: Clustering, health insurance, wardMSC2020: 62H30

Learning reaction coordinates for enhanced sampling directly using atomic positions.

We present an approach for learning a reaction coordinate that can be used to enhance sampling directly between two biomolecular structures. The current method is based on our previous molecular simulation clustering algorithm entitled Size-and-Shape Space Gaussian Mixture Models (shapeGMM). By overcoming rotational invariance using robust alignment protocols, shapeGMM clusters molecular structures using particle positions. The result of shapeGMM is a set of cluster means and covariances that, unlike many internal coordinate methods, do not overdetermine the structures. shapeGMM was found to produce physically meaningful clusters on a 300-microsecond trajectory of a fast-folding HP35 Nle/Nle mutant; the trajectory was obtained from D. E. Shaw Research Institute. To further study the folding and unfolding pathways of this system, we compute a reaction coordinate between corresponding cluster means using Linear Discriminate Analysis (LDA); LDA is a supervised classification technique that identifies the linear coordinate which simultaneously maximizes the between cluster variance and minimizes the within cluster variance. Barriers in the free energy surface (FES) along the first linear discriminant (LD1) corresponded to the point where the committor is 0.5, suggesting this is a good reaction coordinate. Metadynamics simulations biased along LD1, a method we have implemented in PLUMED, achieved a FES in reasonable agreement with that generated from the long MD simulation, suggesting that LD1 is also a good sampling coordinate. FESs produced along coordinates obtained from subsets of the data close to the centers of the cluster means also agree well with the committor, suggesting this approach may be appropriate for situations where both states of a molecule are known, but extensive unbiased data is not available.

DDCAL: Evenly Distributing Data into Low Variance Clusters Based on Iterative Feature Scaling

This work studies the problem of clustering one-dimensional data points such that they are evenly distributed over a given number of low variance clusters. One application is the visualization of data on choropleth maps or on business process models, but without over-emphasizing outliers. This enables the detection and differentiation of smaller clusters. The problem is tackled based on a heuristic algorithm called DDCAL (1d distribution cluster algorithm) that is based on iterative feature scaling which generates stable results of clusters. The effectiveness of the DDCAL algorithm is shown based on 5 artificial data sets with different distributions and 4 real-world data sets reflecting different use cases. Moreover, the results from DDCAL, by using these data sets, are compared to 11 existing clustering algorithms. The application of the DDCAL algorithm is illustrated through the visualization of pandemic and population data on choropleth maps as well as process mining results on process models.

Features of lumbar spine texture extracted from routine MRI correlate with bone mineral density and can potentially differentiate patients with and without fragility fractures in the spine

The use of routine magnetic resonance imaging (MRI) to potentially assess skeletal fragility has been widely studied in osteoporosis. The aim of this study was to evaluate bone texture attributes (TA) from routine lumbar spine (LS) MRI and their correlation with vertebral fragility fractures (VFF) and bone mineral density (BMD). Sixty-four post-menopausal women were submitted to LS densitometry, total spine radiographs, and routine T2-weighted LS MRI. Twenty-two TA were extracted with the platform IBEX from L3 vertebra. The statistical difference was evaluated using ANOVA and Duncan's post-test. Correlation analyses were performed using Spearman's coefficient. Statistical significance was considered when P<0.05. The results did not show a significant difference in BMD between the women with and without fractures. Two bone TA (cluster tendency and variance) were significantly lower in the fracture group. Cluster tendency with VFF in osteopenia was 1.54±1.37 and in osteoporosis was 1.11±58. Cluster tendency without VFF in osteopenia was 2.23±1.38 and in osteoporosis was 1.88±1.14). Variance with VFF in osteopenia was 1.44±1.37 and in osteoporosis was 1.13±59. Variance without VFF in osteopenia was 2.34±1.38 and in osteoporosis was 1.89±1.14. There was a significant correlation between BMD and cluster prominence (r=0.409), cluster tendency (r=0.345), correlation (r=0.570), entropy (r=0.364), information measure corr1 (r=0.378), inverse variance (r=0.449), sum entropy (r=0.320), variance (r=0.338), sum average (r=-0.274), and sum variance (r=-0.266). Our results demonstrated the potential use of TA extracted from routine MRI as a biomarker to assess osteoporosis and identify the tendency of skeletal fragility vertebral fractures.

Distribution Pattern of Fish Richness in the Yarlung Zangbo River Basin

Global warming significantly affects plateau glaciers and surface runoff, and fish are bound to be severely affected. Additionally, an increasing number of human activities (e.g., free captive animals, aquaculture) have led to vulnerable plateau ecosystems being affected by invasive species. To address the above issues, we collected the currently published fish distribution data, and for the first time constructed a richness and fluvial system distribution map of the Yarlung Zangbo River fish (4 orders, 10 families, and 61 species). Based on fish richness and the fluvial system, the native fish in the Yarlung Zangbo River Basin were divided into three clusters, and the non-native fish were divided into six clusters by using Ward’s minimum variance clustering and non-metric multidimensional scaling (NMDS). Environmental factors related to native or non-native fish richness were selected by the random forest model from 21 environmental factors. Then, the relationship between fish richness and environmental factors was explained by the generalized linear model (GLM). Our results showed that the native fish distribution pattern was different from the non-native fish distribution, but their high richness areas were overlapped. Furthermore, native fish richness responds differently than non-native fish richness to environmental factors. The results provided eco-solutions for the conservation and management of fish biodiversity and natural resources in the Yarlung Zangbo River.

Analysis of Self-Organizing Maps and Fuzzy C-Means methods in Clustering Teacher Data for Nominations of Candidates for Education Unit Supervisors

The quality of education is very dependent on the management of education management one of the important factors in the management of education is monitoring and evaluation. Therefore, continuous Education Supervision supported by the appointment of competent supervisors will have implications for the quality of education. Supervision of education management is carried out by the principal or teacher appointed to carry out managerial and academic supervision in certain educational units. The problem that often occurs is that grouping teacher data to be selected as supervisor candidates are still conventional. Therefore, a teacher data grouping model is needed to obtain useful information in planning strategic steps and policy regulations for determining prospective supervisors for academic units. By utilizing Information and Communication Technology, especially in the field of Data Mining, this Teacher Data Grouping research uses the Fuzzy C-Means and Self Organizing Maps method, and the grouping results are analyzed by measuring the spread of data on each grouping formed by using cluster variance. The output of the Teacher Data grouping process using the Fuzzy C-Means and Self Organizing Maps methods can bring up a group of Teacher Data nominations that are competent to be selected as candidates for supervisors for certain educational units. The research results were obtained by forming several groupings in Fuzzy C-Means by providing an error accuracy value of 0.1 and Self Organizing Maps, which is set by the learning rate and learning rate; the results obtained are grouping with 3 clusters by providing a learning rate of 0.8 and a learning rate of 0.7 in the Self Organizing Maps method has a Variant value that is ideal compared to grouping on Fuzzy C-Means and rather than the same method by forming different groups.

Single Tube Molecular Profiling of ALL Samples Reveals Integrated Molecular Signature and Highlights Gene Regulatory Networks

Background: Over 20 acute lymphoblastic leukemia (ALL) subtypes are defined by molecular signature, which is historically mainly dependent on genomic rearrangements (translocations and gene fusions). Advancements in NGS strategies allow high-resolution profiling, taking in to account also gene expression patterns, single nucleotide variants (SNVs) and insertions and deletions (INDELs). Previous studies showed that molecular signature correlates to prognosis and treatment compatibility, making accrue molecular profiling a major interest point for both clinical and research communities, helping identify novel therapeutic targets and designing clinical management approaches. Many current NGS approaches rely on panels-based assays, which screen DNA or RNA to provide one piece of the puzzle, resulting in higher cost and processing time and requiring larger initial inputs. We report a single tube NGS panel that uses total nucleic acid as input for simultaneous screening of DNA and RNA, allowing for simultaneous profiling of gene expression, fusions and variants, establishing a work frame for comprehensive molecular profiling within a single assay. Methods: This study used 102 ALL clinical samples, 85 bone marrow (BM) and 17 peripheral blood (PB). Libraries were prepared using a custom Qiagen Multimodal NGS panel targeting 302 DNA targets and 229 RNA fusions. Enriched amplicon libraries were sequenced with unique dual indices on Illumina NovaSeq 6000. Data were analyzed on our in-house bioinformatic pipeline. Expression values were normalized to GUSB. Previous in-house studies were used to establish baseline cut-offs for upregulation of 31 genes known to be associated with ALL. Variants enrichment for genes was done by normalizing the number of variants to the size of the gene and genes with elevated variant loads were identified, to highlight molecular pathways within our cohort. Results: Since expression can be modulated by both genomic variants and rearrangements, we performed a principal component analysis (PCA) on our RNA expression data to identify the clustering effect within the samples that could be driven by either or both of these genetic factors. The PCA identified several distinct clusters, with PC1 accounting for 39.3% of the variance, and PC2 accounting for 15.3%. The biological source (BM vs. PB) did not have an impact on the clusters, allowing us to analyze both sample types together. We then investigated whether the predetermined 31 over-expressed genes could be driving the clusters. Three clear groups were observed based on over-expression quantification. One group with low overexpression levels (0-1 genes), the second with medium levels (8-11), and one with high-level of over expression (>16 genes). These clusters also corresponded to T-ALL (high variance)-vs B-ALL (low to medium variance) classification, and showed clear segregation of samples with over-expression of EPOR (high variance cluster) from the rest of the samples. RNA expression data also showed a high correlation between expression of PAX5, RUNX1 and TCF3; all are known to interact as part of a gene regulatory network, highlighting its importance to ALL development. Finally, we investigated SNVs' role within the different clusters. While we could not identify any single variant/gene to be especially enriched with any of the clusters, we observed that KMT2D has an enrichment in variants within our cohort, underscoring that epigenetics is an important component of the disease landscape. Conclusions: We demonstrate the use of a single tube multimodal NGS assay for comprehensive genomic profiling. Our analysis shows that this powerful and cost-effective tool can help identify molecular signatures, classify ALL sub-types, identify regulatory networks and molecular mechanisms, which will help not only properly diagnose but also identify and develop therapeutic targets clinical management strategies.

Dynamic cluster structure and predictive modelling of music creation style distributions.

We investigate the dynamics of music creation style distributions to understand cultural evolution involving intelligence to create complex artefacts. Previous work suggested that a music creation style can be quantified as statistics describing a generative process of music data, and that the distribution of music creation styles in a society has cluster structure related to the presence of different musical genres. To find patterns in the dynamics of the cluster structure, we analysed statistics of melodies in Japanese popular music data and statistics of audio features in American popular music data. Using statistical modelling methods, we found that intra-cluster dynamics, such as the contraction and the shift of a cluster, as well as inter-cluster dynamics represented by clusters' relative frequencies, often exhibit notable dynamical modes. Additionally, to compare the individual contributions of these different dynamical aspects for predicting future creation style distributions, we constructed a fitness-based evolutionary model and found that the predictions of cluster frequencies and cluster variances often have comparable contributions. Our results highlight the relevance of intra-cluster dynamics in music style evolution, which have often been overlooked in previous studies. The present methodology can be applied to cultural artefacts whose generative process can be characterized by a discrete probability distribution.