Clustered Data Research Articles

Communicating exploratory unsupervised machine learning analysis in age clustering for paediatric disease

BackgroundDespite the increasing availability of electronic healthcare record (EHR) data and wide availability of plug-and-play machine learning (ML) Application Programming Interfaces, the adoption of data-driven decision-making within routine hospital workflows...

Data Mining Utilizing Various Leveled Clustering Procedures on the Position of Workers in a Data Innovation Firm

The reason of this paper is to clarify dynamic clustering, the divisive and agglomerative dynamic clustering techniques. It fundamentally centers on the concept of the divisive different leveled shapes as well known as the top-down approach by creating a workflow appear, dendrograms, clustered data table which accumulated the clusters based the chosen property, and appear the isolated between each cluster with the assistance of an data mining device called Python. The DIANA dynamic approach utilized data tests of the list of laborers in a Data Advancement firm to induce clusters from the position column inside the data test table. In this work, we in addition executed genuine infers by creating barchart that shows up the ages of the chosen agent sets plotted against the positions which are the Engineers, Assistants, Workers and Troughs.

Total Synthesis Facilitates In Vitro Reconstitution of the C-S Bond-Forming P450 in Griseoviridin Biosynthesis.

Griseoviridin is a group A streptogramin natural product from Streptomyces with broad-spectrum antibacterial activity. A hybrid polyketide-nonribosomal peptide, it comprises a 23-membered macrocycle, an embedded oxazole motif, and a macrolactone with a unique ene-thiol linkage. Recent analysis of the griseoviridin biosynthetic gene cluster implicated SgvP, a cytochrome P450 monooxygenase, in late-stage installation of the critical C-S bond. While genetic and crystallographic experiments provided indirect evidence to support this hypothesis, the exact function of SgvP has never been confirmed biochemically. Herein, we report a convergent total synthesis of pre-griseoviridin, the putative substrate of P450 SgvP and precursor to griseoviridin. Our strategy features concise and rapid assembly of two fragments joined via sequential peptide coupling and Stille macrocyclization. Access to pre-griseoviridin then enabled in vitro validation of SgvP as the C-S bond-forming P450 during griseoviridin biosynthesis, culminating in a nine-step chemoenzymatic synthesis of griseoviridin.

Longer-term Effect of Physical Activity Intervention on Sleep Quality: A Secondary Analysis of Cluster Randomized Controlled Trial.

To examine the longer-term effect of physical activity (PA) intervention on sleep quality and whether the effect was heterogeneous between daytime nappers and non-nappers. This study was a secondary analysis of a cluster randomized controlled trial in China. Eight villages were randomized 1:1 to intervention or control group. The intervention group received an 8-week PA intervention, while the control group did not. The primary outcome of this study was the change in the Pittsburgh Sleep Quality Index (PSQI) global score at 24 months. The 511 participants had a mean age of 70.94 years (SD 5.73) and 55.6% were female. The intervention showed improvements in the PSQI global score at 8 weeks (adjusted mean difference -1.05; P=0.002), and the effect diminished at 24 months (-0.64; P=0.06). There were statistically significant improvements in the PSQI global score for daytime nappers, but not for non-nappers at 8 weeks (adjusted mean difference -0.98; P=0.01 vs -1.27; P=0.05), 12 months (-0.86; P=0.03 vs -0.84; P=0.21), and 24 months (-0.80; P=0.04 vs -0.14; P=0.84), although these improvements were below the minimum detectible level of the PSQI which is 1 point. The 8-week PA intervention was effective in improving sleep quality, while the effect was diminished and below the minimum detectible level of the PSQI which is 1 point after 24 months. The effect of PA intervention on sleep quality was more pronounced in daytime nappers. Additional interventions (e.g., focusing on multiple behavioral interventions such as PA and healthy diet) are needed to maintain the beneficial effect of PA on sleep quality in the general older populations. Further research is required to confirm the mechanisms of the effect of napping and develop tailored interventions.

Deciphering the impact of aging on splenic endothelial cell heterogeneity and immunosenescence through single-cell RNA sequencing analysis

BackgroundAging is associated with significant structural and functional changes in the spleen, leading to immunosenescence, yet the detailed effects on splenic vascular endothelial cells (ECs) and their immunomodulatory roles are not fully understood. In this study, a single-cell RNA (scRNA) atlas of EC transcriptomes from young and aged mouse spleens was constructed to reveal age-related molecular changes, including increased inflammation and reduced vascular development and also the potential interaction between splenic endothelial cells and immune cells.ResultsTen clusters of splenic endothelial cells were identified. DEGs analysis across different EC clusters revealed the molecular changes with aging, showing the increase in the overall inflammatory microenvironment and the loss in vascular development function of aged ECs. Notably, four EC clusters with immunological functions were identified, suggesting an Endothelial-to-Immune-like Cell Transition (EndICLT) potentially driven by aging. Pseudotime analysis of the Immunology4 cluster further indicated a possible aging-induced transitional state, potentially initiated by Ctss gene activation. Finally, the effects of aging on cell signaling communication between different EC clusters and immune cells were analyzed.ConclusionsThis comprehensive atlas elucidates the complex interplay between ECs and immune cells in the aging spleen, offering new insights into endothelial heterogeneity, reprogramming, and the mechanisms of immunosenescence.

BlpU is a broad-spectrum bacteriocin in Streptococcus thermophilus.

Streptococcus thermophilus strain B59671 naturally produces thermophilin 110, a broad-spectrum bacteriocin encoded within the bacteriocin-like peptide (blp) gene cluster, and thermophilin 13 from a separate chromosomal locus. Analysis of the blp gene cluster revealed two genes, blpU and blpK, as potentially encoding bacteriocins. Deletion of blpK from the B59671 chromosome did not result in a loss of antimicrobial activity against either S. thermophilus ST113 or Pediococcus acidilactici F. A deletion mutant of blpU could not be generated in B59671, but the mature BlpU peptide obtained through overexpression in E. coli BL21 or chemical synthesis inhibited the growth of S. thermophilus strains, Streptococcus mutans UA159, P. acidilactici F, and Listeria innocua GV9 L-S, evidencing as a broad-spectrum bacteriocin that does not require modification for activity. This study also showed that the transcription of blpU was approximately 16-fold higher in B59671 than in an induced culture of S. thermophilus LMD-9, which produces a blp-encoded bacteriocin. The increased expression of BlpU in B59671 may explain the unique antimicrobial spectrum associated with this strain. Additionally, it was shown that a blpC deletion mutant of B59671, which prevents expression of BlpU and BlpK, inhibited the growth of other S. thermophilus strains and Bacillus cereus, suggesting that thermophilin 13 produced by B59671 possessed both intra- and interspecies antimicrobial activity. While this study confirmed that BlpU can function as an independent antimicrobial peptide, further studies are required to determine if BlpK can function independently as a broad-spectrum antimicrobial.

Multi-organ immune-related adverse events from immune checkpoint inhibitors and their downstream implications: a retrospective multicohort study

Understanding co-occurrence patterns and prognostic implications of immune-related adverse events is crucial for immunotherapy management. However, previous studies have been limited by sample size and generalisability. In this study, we leveraged a multi-institutional cohort and a population-level database to investigate co-occurrence patterns of and survival outcomes after multi-organ immune-related adverse events among recipients of immune checkpoint inhibitors. In this retrospective study, we identified individuals who received immune checkpoint inhibitors between May 31, 2015, and June 29, 2022, from the Massachusetts General Hospital, Brigham and Women's Hospital, and Dana-Farber Cancer Institute (Boston, MA, USA; MGBD cohort), and between April 30, 2010, and Oct 11, 2021, from the independent US population-based TriNetX network. We identified recipients from all datasets using medication codes and names of seven common immune checkpoint inhibitors, and patients were excluded from our analysis if they had incomplete information (eg, diagnosis and medication records) or if they initiated immune checkpoint inhibitor therapy after Oct 11, 2021. Eligible patients from the MGBD cohort were then propensity score matched with recipients of immune checkpoint inhibitors from the TriNetX database (1:2) based on demographic, cancer, and immune checkpoint inhibitor characteristics to facilitate cohort comparability. We applied immune-related adverse event identification rules to identify patients who did and did not have immune-related adverse events in the matched cohorts. To reduce the likelihood of false positives, patients diagnosed with suspected immune-related adverse events within 3 months after chemotherapy were excluded. We performed pairwise correlation analyses, non-negative matrix factorisation, and hierarchical clustering to identify co-occurrence patterns in the MGBD cohort. We conducted landmark overall survival analyses for patient clusters based on predominant immune-related adverse event factors and calculated accompanying hazard ratios (HRs) and 95% CIs, focusing on the 6-month landmark time for primary analyses. We validated our findings using the TriNetX cohort. We identified 15 246 recipients of immune checkpoint inhibitors from MGBD and 50 503 from TriNetX, of whom 13 086 from MGBD and 26 172 from TriNetX were included in our propensity score-matched cohort. Median follow-up durations were 317 days (IQR 113-712) in patients from MGBD and 249 days (91-616) in patients from TriNetX. After applying immune-related adverse event identification rules, 8704 recipients of immune checkpoint inhibitors were retained from MGBD, of whom 3284 (37·7%) had and 5420 (62·3%) did not have immune-related adverse events, and 18 162 recipients were retained from TriNetX, of whom 5538 (30·5%) had and 12 624 (69·5%) did not have immune-related adverse events. In both cohorts, positive pairwise correlations of immune-related adverse events were commonly observed. Co-occurring immune-related adverse events were decomposed into seven factors across organs, revealing seven distinct patient clusters (endocrine, cutaneous, respiratory, gastrointestinal, hepatic, musculoskeletal, and neurological). In the MGBD cohort, the patient clusters that predominantly had endocrine (HR 0·53 [95% CI 0·40-0·70], p<0·0001) and cutaneous (0·61 [0·46-0·81], p=0·0007) immune-related adverse events had favourable overall survival outcomes at the 6-month landmark timepoint, while the other clusters either had unfavourable (respiratory: 1·60 [1·25-2·03], p=0·0001) or neutral survival outcomes (gastrointestinal: 0·86 [0·67-1·10], p=0·23; musculoskeletal: 0·97 [0·78-1·21], p=0·78; hepatic: 1·20 [0·91-1·59], p=0·19; and neurological: 1·30 [0·97-1·74], p=0·074). Similar results were found in the TriNetX cohort (endocrine: HR 0·75 [95% CI 0·60-0·93], p=0·0078; cutaneous: 0·62 [0·48-0·82], p=0·0007; respiratory: 1·21 [1·00-1·46], p=0·044), except for the neurological cluster having unfavourable (rather than neutral) survival outcomes (1·30 [1·06-1·59], p=0·013). Reliably identifying the immune-related adverse event cluster to which a patient belongs can provide valuable clinical information for prognosticating outcomes of immunotherapy. These insights can be leveraged to counsel patients on the clinical impact of their individual constellation of immune-related adverse events and ultimately develop more personalised surveillance and mitigation strategies. US National Institutes of Health.

Functional Identification and Genetic Analysis of O-Antigen Gene Clusters of Food-Borne Pathogen Yersinia enterocolitica O:10 and Other Uncommon Serotypes, Further Revealing Their Virulence Profiles.

Yersinia enterocolitica is a globally distributed food-borne gastrointestinal pathogen. The O-antigen variation-determined serotype is an important characteristic of Y. enterocolitica, allowing intraspecies classification for diagnosis and epidemiology purposes. Among the 11 serotypes associated with human yersiniosis, O:3, O:5,27, O:8, and O:9 are the most prevalent, and their O-antigen gene clusters have been well defined. In addition to the O-antigen, several virulence factors are involved in infection and pathogenesis of Y. enterocolitica strains, and these are closely related to their biotypes, reflecting pathogenic properties. In this study, we identified the O-AGC of a Y. enterocolitica strain WL-21 of serotype O:10, and confirmed its functionality in O-antigen synthesis. Furthermore, we analyzed in silico the putative O-AGCs of uncommon serotypes, and found that the O-AGCs of Y. enterocolitica were divided into two genetic patterns: (1) O-AGC within the hemH-gsk locus, possibly synthesizing the O-antigen via the Wzx/Wzy dependent pathway, and (2) O-AGC within the dcuC-galU-galF locus, very likely assembling the O-antigen via the ABC transporter dependent pathway. By screening the virulence genes against genomes from GenBank, we discovered that strains representing different serotypes were grouped according to different virulence gene profiles, indicating strong links between serotypes and virulence markers and implying an interaction between them and the synergistic effect in pathogenicity. Our study provides a framework for further research on the origin and evolution of O-AGCs from Y. enterocolitica, as well as on differences in virulent mechanisms among distinct serotypes.

Multi-biobank Mendelian randomization analyses identify opposing pathways in plasma low-density lipoprotein-cholesterol lowering and gallstone disease

Plasma low-density lipoprotein (LDL)-cholesterol is positively associated with coronary artery disease risk while biliary cholesterol promotes gallstone formation. Different plasma LDL-cholesterol lowering pathways may have distinct effects on biliary cholesterol and thereby gallstone disease risk. We conducted a Mendelian randomization (MR) study using data from the UK Biobank (30,547 gallstone disease cases/336,742 controls), FinnGen (34,461 cases/301,383 controls) and Biobank Japan (9,305 cases/168,253 controls). We first performed drug-target MR analyses substantiated by colocalization to investigate the effects of plasma LDL-cholesterol lowering therapies on gallstone disease risk. We then performed clustered MR analyses and pathway analyses to identify distinct mechanisms underlying the association of plasma LDL-cholesterol with gallstone disease risk. For a 1-standard deviation reduction in plasma LDL-cholesterol, genetic mimics of statins were associated with lower gallstone disease risk (odds ratio 0.72 [95% confidence interval 0.62, 0.83]), but genetic mimics of PCSK9 inhibitors and targeting apolipoprotein B were associated with higher risk (1.11 [1.03, 1.19] and 1.23 [1.13, 1.35]). The association for statins was supported by colocalization (posterior probability 98.7%). Clustered MR analyses identified variant clusters showing opposing associations of plasma LDL-cholesterol with gallstone disease risk, with some evidence for ancestry-and sex-specific associations. Among variants lowering plasma LDL-cholesterol, those associated with lower gallstone disease risk were mapped to glycosphingolipid biosynthesis pathway, while those associated with higher risk were mapped to pathways relating to plasma lipoprotein assembly, remodelling, and clearance and ATP-binding cassette transporters. This MR study provides genetic evidence that different plasma LDL-cholesterol lowering pathways have opposing effects on gallstone disease risk.

Cretaceous Fe-oxidising microbial mats in injection dykes in the Variscan gold-bearing metamorphic basement of the Tisza Mega-unit (SE Hungary)

Large quantities of Cretaceous Fe-oxidising bacteria (FeOB) are documented here for the first time in carbonate injection dykes. These injection dykes occur in fissures of a Palaeozoic metamorphic complex in the Geresd Hills, Hungary, and are made up of microlaminated haematitic carbonate that contain very well preserved helical FeOB stalk fossils similar to recent iron-oxidising Gallionella and Mariprofundus. The presence of filament clusters with semi-parallel textures suggests aerotactic bacterial cell movements. Fractal analysis of the filament clusters showed higher fractal dimension values (Df ∼ 1.9) compared to the typical fractal dimension values of the texturally similar, but inorganic Diffusion-Limited Aggregation (DLA) structures (Df ∼ 1.7). This finding is interpreted to suggest that the observed filamentous texture is of microbial origin. Cretaceous basaltic intrusions that penetrated the gold-bearing pyritic metamorphic basement launched a low-temperature flow of reductive iron-rich fluids. Well‑oxygenated bottom waters percolated through thin pelagic sediments and picked up ferrous iron from the pyritic metamorphic bedrock and made it available for the Fe-oxidising microbes. Reactivation of a major fault zone (Mecsekalja Zone) resulted in the development of extensional faults in the metamorphic basement beneath the seafloor. Pelagic carbonate sediments with FeOB mats were drawn into these fissures, forming a network of injection dykes. This study shows that fractal dimension analysis and directional analysis may be a helpful method to differentiate FeOB textures from inorganic textures in ancient rocks. The presented results are expected to provide useful analogues to aid the reconstruction of similar palaeoenvironments worldwide.

Clustering analysis across different speeds reveals two distinct running techniques with no differences in running economy

ABSTRACT Establishing the links between running technique and economy remains elusive due to high inter-individual variability. Clustering runners by technique may enable tailored training recommendations, yet it is unclear if different techniques are equally economical and whether clusters are speed-dependent. This study aimed to identify clusters of runners based on technique and to compare cluster kinematics and running economy. Additionally, we examined the agreement of clustering partitions of the same runners at different speeds. Trunk and lower-body kinematics were captured from 84 trained runners at different speeds on a treadmill. We used Principal Component Analysis for dimensionality reduction and agglomerative hierarchical clustering to identify groups of runners with a similar technique, and we evaluated cluster agreement across speeds. Clustering runners at different speeds independently produced different partitions, suggesting single speed clustering can fail to capture the full speed profile of a runner. The two clusters identified using data from the whole range of speeds showed differences in pelvis tilt and duty factor. In agreement with self-optimisation theories, there were no differences in running economy, and no differences in participants’ characteristics between clusters. Considering inter-individual technique variability may enhance the efficacy of training designs as opposed to ‘one size fits all’ approaches.

Examining ALS: reformed PCA and random forest for effective detection of ALS

ALS (Amyotrophic Lateral Sclerosis) is a fatal neurodegenerative disease of the human motor system. It is a group of progressive diseases that affects the nerve cells in the brain and spinal cord that control the muscle movement of the body hence, detection and classification of ALS at the right time is considered to be one of the vital aspects that can save the life of humans. Therefore, in various studies, different AI techniques are used for the detection of ALS, however, these methods are considered to be ineffectual in terms of identifying the disease due to the employment of ineffective algorithms. Hence, the proposed model utilizes Modified Principal Component Analysis (MPCA) and Modified Random Forest (MRF) for performing dimensionality reduction of all the potential features considered for effective classification of the ALS presence and absence of ALS causing mutation in the corresponding gene. The MPCA is adapted for capturing all the Low-Importance Data transformation. Furthermore, The MPCA is objected to performing three various approaches: Covariance Matrix Correlation, Eigen Vector- Eigenvalue decomposition, and selecting the desired principal components. This is done in aspects of implying the LI (Lower-Importance) Data Transformation. By choosing these potential components without any loss of features ensures better viability of selecting the attributes for ALS-causing gene classification. This is followed by the classification of the proposed model by using Modified RF by updating the clump detector technique. The clump detector is proceeded by clustering approach using K-means, and the data reduced by their dimension are grouped accordingly. These clustered data are analyzed either for ALS causing or devoid of causing ALS. Finally, the model’s performance is assessed using different evaluation metrics like accuracy, recall, F1 score, and precision, and the proposed model is further compared with the existing models to assess the efficacy of the proposed model.

Privacy Preserving Human Mobility Generation Using Grid-Based Data and Graph Autoencoders

This paper proposes a one-to-one trajectory synthetization method with stable long-term individual mobility behavior based on a generalizable area embedding. Previous methods concentrate on producing highly detailed data on short-term and restricted areas for, e.g., autonomous driving scenarios. Another possibility consists of city-wide and beyond scales that can be used to predict general traffic flows. The now-presented approach takes the tracked mobility behavior of individuals and creates coherent synthetic mobility data. These generated data reflect the person’s long-term mobility behavior, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step of the original data, the area is distributed into a geospatial grid structure (H3 is used here). The neighborhood relationships between the grids are interpreted as a graph. A feed-forward autoencoder and a graph encoding–decoding network generate a latent space representation of the area. The original clustered data are associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are generated as top-level patterns for individual mobility behavior. Based on the original data, concrete locations within the new grids are found and connected to ways. The goal is to generate a dataset that shows equivalence in aggregated characteristics and distances in comparison with the original data. The described method is applied to a sample of 120 from a study with 1000 participants whose mobility data were generated in the city of Munich in Germany. The results show the applicability of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result comprises a sharable dataset on the same abstraction level as the input data, which can be beneficial for different applications, particularly for machine learning.

Genome-wide data reveal bi-direction and asymmetrical hybridization origin of a fern species Microlepia matthewii.

Natural hybridization is common and plays a crucial role in driving biodiversity in nature. Despite its significance, the understanding of hybridization in ferns remains inadequate. Therefore, it is imperative to study fern hybridization to gain a more comprehensive understanding of fern biodiversity. Our study delves into the role of hybridization in shaping fern species, employing Microlepia matthewii as a case study to investigate its origins of hybridization. We performed double digest Genotyping-by-sequencing (dd-GBS) on M. matthewii and its potential parent species, identifying nuclear and chloroplast SNPs. Initially, nuclear SNPs were employed to construct the three cluster analysis: phylogenetic tree, principal component analysis, and population structure analysis. Subsequently, to confirm whether the observed genetic mixture pattern resulted from hybridization, we utilized two methods: ABBA-BABA statistical values in the D-suite program and gene frequency covariance in the Treemix software to detect gene flow. Finally, we employed chloroplast SNPs to construct a phylogenetic tree, tracing the maternal origin. The analysis of the nuclear SNP cluster revealed that M. matthewii possesses a genetic composition that is a combination of M. hancei and M. calvescens. Furthermore, the analysis provided strong evidence of significant gene flow signatures from the parental species to the hybrid, as indicated by the two gene flow analyses. The samples of M. matthewii cluster separately with M. hancei or M. calvescens on the chloroplast systematic tree. However, the parentage ratio significantly differs from 1:1, suggesting that M. matthewii is a bidirectional and asymmetrical hybrid offspring of M. hancei and M. calvescens.

Organ Involvement Related to Death in Critically Ill Patients With Leptospirosis: Unsupervised Analysis in a French West Indies ICU.

To identify distinct phenotypes of critically ill leptospirosis patients upon ICU admission and their potential associations with outcome. Retrospective observational study including all patients with biologically confirmed leptospirosis admitted to the ICU between January 2014 and December 2022. Subgroups of patients with similar clinical profiles were identified by unsupervised clustering (factor analysis for mixed data and hierarchical clustering on principal components). All patients admitted to the ICU of the University Hospital of Guadeloupe on the study period. One hundred thirty critically ill patients with confirmed leptospirosis were included. None. At ICU admission, 34% of the patients had acute respiratory failure, and 26% required invasive mechanical ventilation. Shock was observed in 52% of patients, myocarditis in 41%, and neurological involvement in 20%. Unsupervised clustering identified three clusters-"Weil's Disease" (48%), "neurological leptospirosis" (20%), and "multiple organ failure" (32%)-with different ICU courses and outcomes. Myocarditis and neurological involvement were key components for cluster identification and were significantly associated with death in ICU. Other factors associated with mortality included shock, acute respiratory failure, and requiring renal replacement therapy. Unsupervised analysis of critically ill patients with leptospirosis revealed three patient clusters with distinct phenotypic characteristics and clinical outcomes. These patients should be carefully screened for neurological involvement and myocarditis at ICU admission.

Bacillus Paralicheniformis: A potential candidate for natural antibacterial agents from marine macro-algae associated bacteria.

Antibiotic resistance poses a major global health threat, necessitating new approaches to treat bacterial infections. We isolated bacteria from the brown macroalgae Sargassum swartzii collected along the Indian coast. Of 279 isolates, 47.6% showed antibacterial activity against Staphylococcus aureus and Vibrio parahaemolyticus when screened. Ten potent isolates underwent analysis of biosynthetic gene clusters and 16S rRNA sequencing. Isolate MP-99 carried polyketide synthase (pks) and non-ribosomal peptide synthetase (nrps) genes and exhibited antibacterial effects.

Uncovering wastewater treatment plants as possible sources of legionellosis clusters through spatial statistics approach and environmental analysis.

Wastewater treatment plants (WWTPs) are suspected reservoirs of Legionella pneumophila (Lp). The required aeration and mixing steps lead to the emission and dispersion of bioaerosols potentially harboring Lp. The aim of the project is to evaluate municipal WWTPs as a possible source of legionellosis through the statistical analysis of case clusters. A space-time scanning statistical method was implemented in SaTScan software to identify and analyze WWTPs located within and close to spatiotemporal clusters of legionellosis detected in Quebec between 2016 and 2020. In parallel, WWTPs were ranked according to their pollutant load, flow rate and treatment type. These parameters were used to evaluate the WWTP susceptibility to generate and disperse bioaerosols. Results show that 37 of the 874 WWTPs are located inside a legionellosis cluster study zone, including six of the 40 WWTPs ranked most susceptible. In addition, two susceptible WWTPs located within an extended area of 2.5km from the study zone (2.5-km buffer) were included, for a total of 39 WWTPs. The selected 39 WWTPs were further studied to document proximity of population, dominant wind direction, and surrounding water quality. Samples collected from the influent and the effluent of six selected WWTPs revealed the presence of Legionella spp. in 92.3% of the samples. Lp and Lp serogroupg 1 (Lp sg1) were detected below the limit of quantification in 69% and 46% of the samples, respectively. The presence of Legionella in wastewater and the novel statistical approach presented here provides information to the public health authorities regarding the investigation of WWTPs as a possible source of Legionella exposure, sporadic cases, and clusters of legionellosis.

Dynamic Nuclear Polarization Enhanced Multiple-Quantum Spin Counting of Molecular Assemblies in Vitrified Solutions.

Crystallization pathways are essential to various industrial, geological, and biological processes. In nonclassical nucleation theory, prenucleation clusters (PNCs) form, aggregate, and crystallize to produce higher order assemblies. Microscopy and X-ray techniques have limited utility for PNC analysis due to the small size (0.5-3 nm) and time stability constraints. We present a new approach for analyzing PNC formation based on 31P nuclear magnetic resonance (NMR) spin counting of vitrified molecular assemblies. The use of glassing agents ensures that vitrification generates amorphous aqueous samples and offers conditions for performing dynamic nuclear polarization (DNP)-amplified NMR spectroscopy. We demonstrate that molecular adenosine triphosphate along with crystalline, amorphous, and clustered calcium phosphate materials formed via a nonclassical growth pathway can be differentiated from one another by the number of dipolar coupled 31P spins. We also present an innovative approach for examining spin counting data, demonstrating that a knowledge-based fitting of integer multiples of cosine wave functions, instead of the traditional Fourier transform, provides a more physically meaningful retrieval of the existing frequencies. This is the first report of multiquantum spin counting of assemblies formed in solution as captured under vitrified DNP conditions, which can be useful for future analysis of PNCs and other aqueous molecular clusters.

Clusters of mammalian conserved RNA structures in UTRs associate with RBP binding sites.

RNA secondary structures play essential roles in the formation of the tertiary structure and function of a transcript. Recent genome-wide studies highlight significant potential for RNA structures in the mammalian genome. However, a major challenge is assigning functional roles to these structured RNAs. In this study, we conduct a guilt-by-association analysis of clusters of computationally predicted conserved RNA structure (CRSs) in human untranslated regions (UTRs) to associate them with gene functions. We filtered a broad pool of ∼500 000 human CRSs for UTR overlap, resulting in 4734 and 24 754 CRSs from the 5' and 3' UTR of protein-coding genes, respectively. We separately clustered these CRSs for both sets using RNAscClust, obtaining 793 and 2403 clusters, each containing an average of five CRSs per cluster. We identified overrepresented binding sites for 60 and 43 RNA-binding proteins co-localizing with the clustered CRSs. Furthermore, 104 and 441 clusters from the 5' and 3' UTRs, respectively, showed enrichment for various Gene Ontologies, including biological processes such as 'signal transduction', 'nervous system development', molecular functions like 'transferase activity'and the cellular components such as 'synapse' among others. Our study shows that significant functional insights can be gained by clustering RNA structures based on their structural characteristics.

Analysis of Co-localized Biosynthetic Gene Clusters Identifies a Membrane-Permeabilizing Natural Product.

Combination therapy is an effective strategy to combat antibiotic resistance. Multiple synergistic antimicrobial combinations are produced by enzymes encoded in biosynthetic gene clusters (BGCs) that co-localize on the bacterial genome. This phenomenon led to the hypothesis that mining co-localized BGCs will reveal new synergistic combinations of natural products. Here, we bioinformatically identified 38 pairs of co-localized BGCs, which we predict to produce natural products that are related to known compounds, including polycyclic tetramate macrolactams (PoTeMs). We further showed that ikarugamycin, a PoTeM, increases the membrane permeability of Acinetobacter baumannii and Staphylococcus aureus, which suggests that ikarugamycin might be an adjuvant that facilitates the entry of other natural products. Our work outlines a promising avenue to discover synergistic combinations of natural products by mining bacterial genomes.