Similar Clusters Research Articles

Exploring latitudinal gradients and environmental drivers of amphipod biodiversity patterns regarding depth and habitat variations

Amphipods are known as umbrella species in conservation biology that their protection indirectly protects other species. Recent hypotheses suggest a bimodal latitudinal global species richness pattern for amphipods, irrespective of species’ depth or habitat type. Phylogeographic hypotheses suggested two distinct procedures for amphipod diversification: ecological radiation and Pangea fragmentation. This study aimed to revisit the bimodal latitudinal pattern of species richness for amphipods, investigate similarities in species composition and main environmental amphipod distribution drivers regarding their depth and habitat variations. The comprehensive database was collected from open-access data and a personal sampling database from the Persian Gulf and the Gulf of Oman. After rigorous data quality controls, the final dataset comprised 1,142,416 distribution records of 6,424 accepted marine amphipod species distributed from 0 to 10,900 m depth. All the species were grouped into pelagic and benthic, shallow-water (> 200 m), and deep-sea (< 200 m). Alpha and gamma species richness and the expected number of species per 50 random samples (ES50) were calculated. Our findings showed that species richness patterns in amphipods are shaped not only by habitat types but also by depth, and they are not significantly bimodal in all groups. Also, the Beta diversity of benthic species revealed distinct diversification patterns between benthic and pelagic species. The species similarity clusters revealed a fragmentation between Gondwana and Laurasia for shallow benthic species. However, the similarity between pelagic amphipod communities is more compatible with ecological parameters. Generalized Additive Models (GAMs) highlighted that environmental variables play a significant role in species distributions, for example, temperature and chlorophyll levels were the main predictors of species richness in shallow waters. However, a complete effect of multiple environmental variables is responsible for deep-sea species gradients. These findings are crucial information to be considered when managing the species richness and establishing conservation priorities for their habitats.

CT perfusion for lesion-symptom mapping in large vessel occlusion ischemic stroke

BackgroundIdentifying eloquent regions associated with poor outcomes based on CT perfusion (CTP) may help inform personalized decisions on selection for endovascular therapy (EVT) in patients with large vessel occlusion (LVO)...

Development of HTC-DBSCAN: A Hierarchical Trajectory Clustering Algorithm with Automated Parameter Tuning

Existing route-clustering methods often fail to identify abnormal sections or similarities between routes, mainly when working with large or long datasets. While sub-route clustering can detect regional patterns, it struggles to accurately capture the overall route structure. The present study proposes a new ship route-clustering method that enhances computational efficiency and noise recognition while addressing these limitations. We refined Automatic Identification System data via four data-cleaning processes and applied a statistical distance measurement to assess ship trajectory similarity. Dimensionality reduction was then used to facilitate clustering. The clustering of ship route similarities is non-parametric and can be applied to datasets not separated based on density to find clusters of various densities. Density-Based Spatial Clustering of Applications (DBSCA) applies to many research fields; using the DBSCA with Noise (DBSCAN) algorithm, we propose an improved DBSCAN algorithm that automatically determines the parameters Epsilon and MinPts. In this study, as a core ship route-clustering process, we propose a sub-route clustering process by setting the distance and density of data points to clear standards for re-analysis and completion. The proposed approach demonstrates markedly enhanced clustering performance, offering a more sophisticated and efficient basis for ship route decision-making.

Analysis and control of hybrid convection-radiation drying systems toward energy saving strategy.

Food drying is a vital technology in agricultural preservation, where hybrid drying provides efficient moisture removal. However, the transient behavior of the moisture ratio ( ) and associated drying rate ( ) could increase energy consumption if not managed with an effective energy-saving strategy. This study investigates the impacts of hybrid convection-radiation drying on , , drying time, and energy consumption. Unlike previous studies, which have not fully addressed the connections between operating conditions and drying kinetics, this work provides new insights into these relationships. Additionally, an artificial neural network (ANN) control model is applied to optimize energy consumption, offering a new approach to improving the efficiency of the drying process. During the experiment, the airflow velocity was varied from 0.7m/s to 1.5m/s, the airflow temperature was varied from 40°C to 60°C, and the radiation intensity was varied from 1500W/m2 to 3000W/m2. The results showed that the transient behavior exhibited four data groups with consistent and through the mean and statistical analysis. Increasing radiation intensity and air temperature has decreased the drying time, while higher airflow has increased the drying time. The energy indices were enhanced by increasing radiation intensity and temperature while reducing airflow velocity. The measured of all groups exhibited similar kinetics behavior, while the associated exhibited similar clustering through the self-organizing map. Those findings were further controlled using an ANN model with 99% predicting accuracy. With airborne heating at 60°C and airflow at 0.7m/s, the radiation intensity is transiently controlled, showing a 6.5% drying time reduction and a 36% energy saving. Thus, controlling the radiation intensity and its impact on the slice properties is highly desirable in future work. This work could help designers improve the processing efficiency and energy conservation of garlic slices drying.

Quantum versatility in PageRank

Quantum mechanics empowers the emergence of quantum advantages in various fields, including quantum algorithms. Quantum PageRank is a promising tool for a future quantum internet. Recently, arbitrary phase rotations (APRs) have been introduced in the underlying Szegedy's quantum walk of the quantum PageRank algorithm. In this work we thoroughly study the role APR plays in quantum PageRank. We discover the versatility resulting from quantumness. Specifically, we discover the emergence of a cluster phenomenon in rankings considering the rotation phases, i.e., the existence of similar clusters in the distribution of the rankings and their fidelity with the corresponding classical PageRanks, the ranking distribution variance, the coherence and entanglement of PageRank states, and the power-law parameter in the ranking distributions on a scale-free network concerning the two rotation phases. Furthermore, we propose an alternate quantum PageRank with APR which provides an extra tunnel for the analysis of PageRank. We also study the PageRank on the trackback graph of a scale-free graph for the investigation of network information traffic tracking. We demonstrate the rich cluster diversity formed in our alternate quantum PageRank, which offers a novel perspective on the quantum versatility of PageRank. Our results present the quantum-enabled perspective for PageRanking and shed light on the design and application of practical quantum PageRank algorithms. Published by the American Physical Society 2024

Ficus deltoidea standardized methanolic extract improves kidney structure and function against streptozotocin-induced diabetic nephropathy in rats

BackgroundOxidative stress and peripheral insulin resistance are the key pathogenesis of diabetic nephropathy. We have previously demonstrated that Ficus deltoidea increases insulin secretion and improves tissue regeneration by reducing oxidative stress in diabetic rats. However, it remains uncertain whether F. deltoidea could delay the progression of nephropathy. Hypothesis/PurposeThis study aimed to examine the nephroprotective effects of F. deltoidea on streptozotocin-induced diabetic nephropathy in rats. MethodsHigh-Performance Liquid Chromatography (HPLC) was used to identify the flavonoid compounds in the leaf extract of F. deltoidea. The methanolic extract of F. deltoidea was administered orally at 1000 mg/kg body weight for eight weeks to diabetic rats. Serum creatinine, urea, uric acid, total bilirubin, and urinary creatinine were measured to estimate kidney function. Enzyme-linked immunosorbent assays (ELISA) assessed the levels of oxidative stress, antioxidants, and apoptosis-related proteins in the kidney tissue. The spectral markers related to nephroprotective activity were predicted using Fourier-transform infrared (FTIR) spectroscopy combined with chemometric analysis. Histomorphometric evaluations were performed on the kidney sections stained with hematoxylin and eosin (H&E). ResultsHPLC identified the presence of vitexin, isovitexin, quercetin, and kaempferol in F. deltoidea methanolic leaves extract. The biochemical and histological examinations consistently showed that F. deltoidea extract improved kidney structure and function by reducing oxidative stress and apoptosis. We noticed that biochemical and pathological changes corresponded to the infrared (IR) peaks at 1545 cm-1 and 1511 cm-1 (amide II), 1246 cm-1 (DNA/RNA phosphate backbones), and 1181 cm-1 (C–O vibrations). Multivariate analysis of IR spectra demonstrated that the diabetic rats treated with F. deltoidea extract had a similar clustering pattern to that of the normal animals. ConclusionThese findings indicate that the methanolic extract of F. deltoidea exhibits nephroprotective activity. FTIR spectroscopy may be useful for monitoring structural and biochemical alterations in the kidney during diabetes treatment.

Genetic Characterization of Sumatran Mirah Chicken Based on Mitochondrial D-loop Region Sequence

Mirah chickens are Indonesian indigenous chickens that originate from Simalungun Regency, North Sumatera Province of Indonesia. The study aimed to determine the genetic characterization of Mirah chicken based on the mitochondrial D-loop region (838 bp). Twenty Mirah cocks from Simalungun Regency of Indonesia were used in the present study. The results showed that fourteen haplotypes were found in the studies of birds based on sixteen mutation sites. Therefore, these haplotype and nucleotide diversities in the partial D-loop region of the Mirah chicken were classified as high. The Median-joining tree revealed that the Mirah chickens were classified in a similar cluster with the Red jungle fowl (Gallus gallus). Ten haplotypes of birds were close to G. g. bankiva, and four haplotypes of birds were close to G. g. gallus. In conclusion, Mirah chickens had the genetic introgression from two sub-species of Red junglefowl, i.e., G.g. gallus and G.g. bankiva. Hence, the pure breeding program for Mirah chickens is important to conserve their genetic resources from extinction.

Abstract 4138373: Machine learning identifies clinically distinct phenotypes in patients with aortic regurgitation.

Background: Aortic regurgitation (AR) is a prevalent valve disease with a long latent period to symptoms. Recent data has suggested the role of novel markers of myocardial overload in assessing onset of decompensation. Method: We sought to evaluate the role of unsupervised cluster analyses in identifying different clinical clusters, including clinical status, and a large number of echocardiographic variables including left ventricular (LV) volumes, and their association with mortality. Patients with ≥moderate-severe chronic AR identified using echocardiography at Mayo Clinic, Rochester were retrospectively analyzed. Primary outcome was all-cause mortality censored at aortic valve surgery/last follow-up. Uniform Manifold Approximation and Projection (UMAP) with K -means algorithm was used to cluster patients using clinical and, echocardiographic variables at the time of presentation. Missing data were imputed with the Multiple Imputation by Chained Equations (MICE) method. A supervised approach trained on the training set was used to find cluster membership in a hold-out validation set. Log-rank tests were used to assess differences in mortality rates between the clusters, both in the training and validation sets. Results: Three distinct clusters were identified among 1100 patients (log-rank P for survival &lt;0.001). Cluster 1 (n=337), which included younger males with severe AR but fewer symptoms, showed the best survival, 75.6% (69.5, 82.3). Cluster 2 (n=235), older and more females with elevated filling pressures, showed intermediate survival of 64.2 % (56.8, 72.5). Cluster 3 (n=253), characterized by severe symptomatic AR, demonstrated the lowest survival of 45.3 % (34.4, 59.8) at 5 years. Similar clusters were formed in the internal validation cohort. Conclusion: Distinct clusters with variable echocardiographic features and mortality differences exist within patients with chronic ≥moderate-severe AR. Recognizing these clusters can refine individual risk stratification and clinical decision-making after verification in future prospective studies.

Supporting prescriber progression: a group concept mapping study

Abstract Introduction Education reforms allow pharmacists to become prescribers earlier in their career and from 2026, new registrants will be independent prescribers.1 In NHS Scotland, an operational framework supports the development of pharmacist independent prescribing.2 We wanted to supplement the operational framework by identifying our core areas of prescribing to articulate prescribing progression and inform education and training provision. Aim To develop a core prescribing framework to support safe prescribing and a spiral approach to expanding scope of practice. Methods We conducted a participatory mixed-method approach using group concept mapping methodology.3 Phase 1 (brainstorming): all hospital pharmacists working in a patient-facing clinical role were invited to enter statements on a cloud-based platform (Padlet®) using the prompt ‘In my day-to-day practice I prescribe and/or make prescribing recommendations about the following medicines / conditions …’. The statements were triangulated with electronic inpatient pharmacist prescribing data over the previous three months. Phase 2 (sorting and rating): a project group comprised of pharmacists with different levels of practice and areas of clinical expertise, individually sorted the statements into groups based on the statements’ conceptual similarity and rated each statement by level of practice (post-registration foundation, advancing, advanced) and importance for including in a core framework (5-point Likert scale). R software applied multidimensional scaling to sorting data to create a similarity matrix, cluster map and Go-Zones. ChatGPT generated cluster titles. The project group discussed the results and following a few revisions, consensus was achieved on the content of the prescribing framework. This study was not considered research and was approved by the pharmacy quality improvement team in line with local governance process. Results Forty-three pharmacists participated in the brainstorming phase and generated 158 statements. Fourteen pharmacists participated in the sorting and rating phase. Multidimensional scaling analysis identified three clusters of prescribing activities: initiating, adjusting and deprescribing. The data identified activities involving initiating medicines and autonomous decision making rated at advancing/advanced level practice while adjusting/deprescribing medicines (often with the caveat of within guidelines, protocols or team decisions) was rated at post-registration foundation level. The prescribing framework includes the activities considered important and is organised by cluster and level of practice. Discussion / Conclusion This study provides a structured conceptualisation of prescribing across the levels of practice. The prescribing framework reflects the needs of our patients and hospital inpatient service and provides a scaffolded approach to progressing from inexperienced to more advanced prescribing roles. Post-registration foundation prescribing activities focus on processes or types of prescribing, whilst advanced are more autonomous and complex. Limitations we didn’t collect clinical area of practice as part of the demographic information for the brainstorming phase and some specialities may not be represented. Further work includes a training needs analysis and developing entrustable professional activities for core and higher risk areas of prescribing (e.g. post-operative pain).

Machine Learning Identifies Clinically Distinct Phenotypes in Patients with Aortic Regurgitation.

Aortic regurgitation (AR) is a prevalent valve disease with a long latent period to symptoms. Recent data has suggested the role of novel markers of myocardial overload in assessing onset of decompensation. We sought to evaluate the role of unsupervised cluster analyses in identifying different clinical clusters, including clinical status, and a large number of echocardiographic variables including left ventricular (LV) volumes, and their association with mortality. Patients with ≥moderate-severe chronic AR identified using echocardiography at Mayo Clinic, Rochester were retrospectively analyzed. Primary outcome was all-cause mortality censored at aortic valve surgery/last follow-up. Uniform Manifold Approximation and Projection (UMAP) with K-means algorithm was used to cluster patients using clinical and, echocardiographic variables at the time of presentation. Missing data were imputed with the Multiple Imputation by Chained Equations (MICE) method. A supervised approach trained on the training set was used to find cluster membership in a hold-out validation set. Log-rank tests were used to assess differences in mortality rates between the clusters, both in the training and validation sets. Three distinct clusters were identified among 1100 patients (log-rank P for survival <0.001). Cluster 1 (n=337), which included younger males with severe AR but fewer symptoms, showed the best survival, 75.6% (69.5, 82.3). Cluster 2 (n=235), older and more females with elevated filling pressures, showed intermediate survival of 64.2 % (56.8, 72.5). Cluster 3 (n=253), characterized by severe symptomatic AR, demonstrated the lowest survival of 45.3 % (34.4, 59.8) at 5 years. Similar clusters were formed in the internal validation cohort. Distinct clusters with variable echocardiographic features and mortality differences exist within patients with chronic ≥moderate-severe AR. Recognizing these clusters can refine individual risk stratification and clinical decision-making after verification in future prospective studies.

Lower-mass-gap Black Holes in Dense Star Clusters

The existence of compact stellar remnants in the mass range 2–5 M ⊙ has long been debated. This so-called lower-mass gap (LMG) was initially suggested by the lack of low-mass X-ray binary observations with accretors about 2–5 M ⊙, but it has recently been called into question following newer observations, including an LMG candidate with a millisecond pulsar (MSP) companion in the dense globular cluster NGC 1851. Here, we model NGC 1851 with a grid of similar dense star clusters utilizing the state-of-the-art Monte Carlo N-body code Cluster Monte Carlo, and we specifically study the formation of LMG black holes (BHs). We demonstrate that both massive star evolution and dynamical interactions can contribute to forming LMG BHs. In general, the collapse of massive remnants formed through mergers of neutron stars (NSs) or massive white dwarfs produces the largest number of LMG BHs among all formation channels. However, in more massive clusters, supernova core collapse can contribute comparable numbers. Our NGC 1851-like models can reproduce MSP—LMG BH binaries similar to the observed system. Additionally, the LMG BHs can also become components of dynamically assembled binaries, and some will be in merging BH–NS systems similar to the recently detected gravitational wave source GW230529. However, the corresponding merger rate is probably ≲1 Gpc−3 yr−1.

Ethylene and epoxyethane metabolism in methanotrophic bacteria: comparative genomics and physiological studies using Methylohalobius crimeensis.

The genome of the methanotrophic bacterium Methylohalobius crimeensis strain 10Ki contains a gene cluster that encodes a putative coenzyme-M (CoM)-dependent pathway for oxidation of epoxyethane, based on homology to genes in bacteria that grow on ethylene and propylene as sole substrates. An alkene monooxygenase was not detected in the M. crimeensis genome, so epoxyethane is likely produced from co-oxidation of ethylene by the methane monooxygenase enzyme. Similar gene clusters were detected in about 10% of available genomes from aerobic methanotrophic bacteria, primarily strains grown from rice paddies and other wetlands. The sparse occurrence of the gene cluster across distant phylogenetic groups suggests that multiple lateral gene transfer events have occurred in methanotrophs. In support of this, the gene cluster in M. crimeensis was detected within a large genomic island predicted using multiple methods. Growth studies, reverse transcription-quantitative PCR (RT-qPCR) and proteomics were performed to examine the expression of these genes in M. crimeensis. Growth and methane oxidation activity were completely inhibited by the addition of >0.5% (v/v) ethylene to the headspace of cultures, but at 0.125% and below, the inhibition was only partial, and ethylene was gradually oxidized. The etnE gene encoding epoxyalkane:CoM transferase was strongly upregulated in ethylene-exposed cells based on RT-qPCR. Proteomics analysis confirmed that EtnE and nine other proteins encoded in the same gene cluster became much more predominant after cells were exposed to ethylene. The results suggest that ethylene is strongly inhibitory to M. crimeensis, but the bacterium responds to ethylene exposure by expressing an epoxide oxidation system similar to that used by bacteria that grow on alkenes. In the obligate methanotroph M. crimeensis, this system does not facilitate growth on ethylene but likely alleviates toxicity of epoxyethane formed through ethylene co-oxidation by particulate methane monooxygenase. The presence of predicted epoxide detoxification systems in several other wetland methanotrophs suggests that co-oxidation of ambient ethylene presents a stress for methanotrophic bacteria in these environments and that epoxyethane removal has adaptive value.

A comparative analysis of unsupervised machine-learning methods in PSG-related phenotyping.

Obstructive sleep apnea is a heterogeneous sleep disorder with varying phenotypes. Several studies have already performed cluster analyses to discover various obstructive sleep apnea phenotypic clusters. However, the selection of the clustering method might affect the outputs. Consequently, it is unclear whether similar obstructive sleep apnea clusters can be reproduced using different clustering methods. In this study, we applied four well-known clustering methods: Agglomerative Hierarchical Clustering; K-means; Fuzzy C-means; and Gaussian Mixture Model to a population of 865 suspected obstructive sleep apnea patients. By creating five clusters with each method, we examined the effect of clustering methods on forming obstructive sleep apnea clusters and the differences in their physiological characteristics. We utilized a visualization technique to indicate the cluster formations, Cohen's kappa statistics to find the similarity and agreement between clustering methods, and performance evaluation to compare the clustering performance. As a result, two out of five clusters were distinctly different with all four methods, while three other clusters exhibited overlapping features across all methods. In terms of agreement, Fuzzy C-means and K-means had the strongest (κ = 0.87), and Agglomerative hierarchical clustering and Gaussian Mixture Model had the weakest agreement (κ = 0.51) between each other. The K-means showed the best clustering performance, followed by the Fuzzy C-means in most evaluation criteria. Moreover, Fuzzy C-means showed the greatest potential in handling overlapping clusters compared with other methods. In conclusion, we revealed a direct impact of clustering method selection on the formation and physiological characteristics of obstructive sleep apnea clusters. In addition, we highlighted the capability of soft clustering methods, particularly Fuzzy C-means, in the application of obstructive sleep apnea phenotyping.

Pseudomonas koreensis AB36 from a gold mine: genomic insights into adaptation to extreme conditions and potential plant growth promotion

The genus Pseudomonas is one of the most varied and widespread bacterial genera, with species found in most environment. They are known to degrade organic and inorganic compounds, produce secondary metabolites, and enhance plant growth. The genome of Pseudomonas koreensis AB36, a heavy metal resistant organism isolated from a gold mine was sequenced to unveil the versatile metabolic potential of the organism. The genome is a single circular chromosome of 5,902,614 bp, with G + C content of 60.1%. There are 4154 similar orthologous gene clusters shared among strain AB36 and other sequenced P. koreensis strains with 7 clusters found alone in the genome of strain AB36. Genome mining of the organism predicted 8 biosynthetic gene clusters using antiSMASH including three non-ribosomal peptide synthethase (NRPS) clusters, arylpolyene and bacteriocin. The genome contains putative genes for heavy metal transport/resistance. These results show the heavy metal resistance ability and degradation of xenobiotic compounds of strain AB36 as well as its potential to adapt to various environments.

Dynamics of second-language learners' semantic memory networks: Evidence from a snowball sampling paradigm.

This article provides an analysis of structural changes in second-language (L2)-based semantic memory networks-graphs composed of L2 words as nodes and semantic relations between them as edges, during L2 learning. We used snowball sampling paradigm to create individual semantic networks of participants divided into two groups differing in L2 learning time and then compare their structural characteristics cross-sectionally. The results showed that as L2 learning progresses, semantic memory networks tend to become more connected (by increasing the average node degree), more efficient (by decreasing the average shortest path length), less fragmented (by decreasing the modularity), less centralized (by decreasing the centralization), less dense (by decreasing the density), and no more "small-worlded" (by similar average clustering coefficients and small-world indices). The findings provide quantitative evidence of how the duration of L2 learning shapes the structure of L2-based semantic memory networks generated in the snowball sampling paradigm. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Prediction of post-PCV13 pneumococcal evolution using invasive disease data enhanced by inverse-invasiveness weighting.

After introducing pneumococcal conjugate vaccines (PCVs), serotype replacement occurred in Streptococcus pneumoniae. Predicting which pneumococcal strains will become common in carriage after vaccination can enhance vaccine design, public health interventions, and understanding of pneumococcal evolution. Invasive pneumococcal isolates were collected during 1998-2018 by the Active Bacterial Core surveillance (ABCs). Carriage data from Massachusetts (MA) and Southwest United States were used to calculate weights. Using pre-vaccine data, serotype-specific inverse-invasiveness weights were defined as the ratio of the proportion of the serotype in carriage to the proportion in invasive data. Genomic data were processed under bioinformatic pipelines to define genetically similar sequence clusters (i.e., strains), and accessory genes (COGs) present in 5-95% of isolates. Weights were applied to adjust observed strain proportions and COG frequencies. The negative frequency-dependent selection (NFDS) model predicted strain proportions by calculating the post-vaccine strain composition in the weighted invasive disease population that would best match pre-vaccine COG frequencies. Inverse-invasiveness weighting increased the correlation of COG frequencies between invasive and carriage data in linear or logit scale for pre-vaccine, post-PCV7, and post-PCV13; and between different epochs in the invasive data. Weighting the invasive data significantly improved the NFDS model's accuracy in predicting strain proportions in the carriage population in the post-PCV13 epoch, with the adjusted R2 increasing from 0.254 before weighting to 0.545 after weighting. The weighting system adjusted invasive disease data to better represent the pneumococcal carriage population, allowing the NFDS mechanism to predict strain proportions in carriage in the post-PCV13 epoch. Our methods enrich the value of genomic sequences from invasive disease surveillance.IMPORTANCEStreptococcus pneumoniae, a common colonizer in the human nasopharynx, can cause invasive diseases including pneumonia, bacteremia, and meningitis mostly in children under 5 years or older adults. The PCV7 was introduced in 2000 in the United States within the pediatric population to prevent disease and reduce deaths, followed by PCV13 in 2010, PCV15 in 2022, and PCV20 in 2023. After the removal of vaccine serotypes, the prevalence of carriage remained stable as the vacated pediatric ecological niche was filled with certain non-vaccine serotypes. Predicting which pneumococcal clones, and which serotypes, will be most successful in colonization after vaccination can enhance vaccine design and public health interventions, while also improving our understanding of pneumococcal evolution. While carriage data, which are collected from the pneumococcal population that is competing to colonize and transmit, are most directly relevant to evolutionary studies, invasive disease data are often more plentiful. Previously, evolutionary models based on negative frequency-dependent selection (NFDS) on the accessory genome were shown to predict which non-vaccine strains and serotypes were most successful in colonization following the introduction of PCV7. Here, we show that an inverse-invasiveness weighting system applied to invasive disease surveillance data allows the NFDS model to predict strain proportions in the projected carriage population in the post-PCV13/pre-PCV15 and pre-PCV20 epoch. The significance of our research lies in using a sample of invasive disease surveillance data to extend the use of NFDS as an evolutionary mechanism to predict post-PCV13 population dynamics. This has shown that we can correct for biased sampling that arises from differences in virulence and can enrich the value of genomic data from disease surveillance and advance our understanding of how NFDS impacts carriage population dynamics after both PCV7 and PCV13 vaccination.

Filling the Treatment Gap: Geographic Expansion of Buprenorphine Providers Across the U.S.

ObjectiveWith opioid-related deaths reaching epidemic levels, gaining a better understanding of access to treatment for opioid use disorder (OUD) is critical. Buprenorphine is an effective medication for the treatment of opioid addiction. The analysis is critical post-X-waiver elimination which extended the ability to prescribe buprenorphine for the treatment of OUD to all practitioners with DEA Schedules II-V on their DEA Registration. The primary purpose of the analysis was to explore the geographic patterns of substance use treatment and establish a geographical baseline to assess the removal of the X-Waiver in the future. Data and MethodsWe assessed the expansion of buprenorphine providers across the data from all US counties up to December 31, 2022. We used all certified buprenorphine providers' data from the database of the buprenorphine waiver notification system (BWNS). We used county-level population data from the US Census of American Community Survey (ACS) of 2021 and the CDC's Drug-related mortality data. We implemented spatial scan statistics to identify the spatial clusters using SaTScan. ResultsThe results from our analysis show that Doctor of Medicine/Doctor of Osteopathic Medicine (MD/DO) have the highest numbers of certified providers at 8134 (65.08%) in 2018 and 14525 (57.87%) in 2022. Our analysis shows that the distribution of buprenorphine providers across the counties of the United States was significantly clustered. Higher clusters with RR of more than 1 (p-value <0.001) were found in the states of Washington, Oregon, Northern California, and the Western borders of Montana. Similar clusters of counties with RR more than 1 (p-value <0.001) were found in the northeastern states of Maine, Vermont, and New Hampshire. ConclusionsFrom our analysis, it is evident that buprenorphine-certified providers are clustered in areas of higher drug-related mortality filling a treatment gap. The elimination of the X-waiver will be a significant step towards increasing access to MOUD and this analysis may be used as a geographical baseline to assess in the future.

Atomically Precise Au24Pt(thiolate)12(dithiolate)3 Nanoclusters with Excellent Electrocatalytic Hydrogen Evolution Reactivity.

[Au24Pt(C6)18]0 (C6 = 1-hexanethiolate) is twice as active as commercial Pt nanoparticles in promoting the electrocatalytic hydrogen evolution reaction (HER), thereby attracting attention as new HER catalysts with well-controlled geometric structures. In this study, we succeeded in synthesizing two new Au-Pt alloy nanoclusters, namely, [Au24Pt(TBBT)12(TDT)3]0 (TBBT = 4-tert-butylbenzenethiolate; TDT = thiodithiolate) and [Au24Pt(TBBT)12(PDT)3]0 (PDT = 1,3-propanedithiolate), by exchanging all the ligands of [Au24Pt(PET)18]0 (PET = 2-phenylethanethiolate) with mono- or dithiolates. Although [Au24Pt(TBBT)12(TDT)3]0 was synthesized serendipitously, a similar cluster, [Au24Pt(TBBT)12(PDT)3]0, was subsequently obtained by selecting the appropriate reaction conditions and optimal combination of thiolate and dithiolate ligands. Single crystal X-ray diffraction analyses revealed that the lengths and orientations of -Au(I)-SR-Au(I)- staples in [Au24Pt(TBBT)12(TDT)3]0 and [Au24Pt(TBBT)12(PDT)3]0 were different from those in [Au24Pt(C6)18]0, [Au24Pt(PET)18]0, and [Au24Pt(TBBT)18]0, and these subtle differences were reflected in the geometric and electronic structures as well as the HER activities of [Au24Pt(TBBT)12(TDT)3]0 and [Au24Pt(TBBT)12(PDT)3]0. Accordingly, the HER activities of products [Au24Pt(TBBT)12(TDT)3]0 and [Au24Pt(TBBT)12(PDT)3]0 were, respectively, 3.5 and 4.9 times higher than those of [Au24Pt(C6)18]0 and [Au24Pt(TBBT)18]0.

Long range segmentation of prokaryotic genomes by gene age and functionality.

Bacterial and archaeal genomes encompass numerous operons that typically consist of two to five genes. On larger scales, however, gene order is poorly conserved through the evolution of prokaryotes. Nevertheless, non-random localization of different classes of genes on prokaryotic chromosomes could reflect important functional and evolutionary constraints. We explored the patterns of genomic localization of evolutionarily conserved (ancient) and variable (young) genes across the diversity of bacteria and archaea. Nearly all bacterial and archaeal chromosomes were found to encompass large segments of 100-300 kb that were significantly enriched in either ancient or young genes. Similar clustering of genes with lethal knockout phenotype (essential genes) was observed as well. Mathematical modeling of genome evolution suggests that this long-range gene clustering in prokaryotic chromosomes reflects perpetual genome rearrangement driven by a combination of selective and neutral processes rather than evolutionary conservation.

Development of a Hierarchical Clustering Method for Anomaly Identification and Labelling of Marine Machinery Data

The application of artificial intelligence models for the fault diagnosis of marine machinery increased expeditiously within the shipping industry. This relates to the effectiveness of artificial intelligence in capturing fault patterns in marine systems that are becoming more complex and where the application of traditional methods is becoming unfeasible. However, despite these advances, the lack of fault labelling data is still a major concern due to confidentiality issues, and lack of appropriate data, for instance. In this study, a method based on histogram similarity and hierarchical clustering is proposed as an attempt to label the distinct anomalies and faults that occur in the dataset so that supervised learning can then be implemented. To validate the proposed methodology, a case study on a main engine of a tanker vessel is considered. The results indicate that the method can be a preliminary option to classify and label distinct types of faults and anomalies that may appear in the dataset, as the model achieved an accuracy of approximately 95% for the case study presented.