Set Of Clusters Research Articles

Geographic Virtual Pooling of Hospital Resources: Data-Driven Trade-off Between Waiting and Traveling

Problem definition: Patient-level data from 72 magnetic resonance imaging (MRI) hospitals in Ontario, Canada from 2013 to 2017 show that over 60% of patients exceeded their wait time targets. We conduct a data-driven analysis to quantify the reduction in the patient fraction exceeding (FET) target for MRI services through geographic virtual resource-sharing while limiting incremental driving time. We present a data-driven method to solve the geographic pooling problem of partitioning 72 hospitals with heterogeneous patients with different wait time targets located in a two-dimensional region into a set of clusters. Methodology/results: We propose an “augmented-priority rule,” which is a sequencing rule that balances the patient’s initial priority class and the number of days until her wait time target. We then use neural networks to predict patient arrival and service times. We combine this predicted information and the sequencing rule to implement “advance scheduling,” which informs the patient of her treatment day and location when requesting an MRI scan. We then optimize the number of geographic resource pools among the 72 hospitals using genetic algorithms. Our resource-pooling model lowers the FET from 66% to 36% while constraining the average incremental travel time below three hours. In addition, our model shows that only 10 additional scanners are needed to achieve 10% FET, whereas 50 additional scanners would be needed without resource sharing. Over 70% of the hospitals are not worse off financially. Each individual hospital, measured over at least two weeks, achieves a higher machine utilization and a lower FET. Managerial implications: Our paper provides a practical, data-driven geographical resource-sharing model that hospitals can readily implement. Our method achieves a near-optimal solution with low computational complexity. Using smart data-driven scheduling, a little extra capacity placed at the right location is all we need to achieve the desired FET under geographic resource-sharing. Funding: This paper is supported by the following grant: Canadian Institutes of Health Research (CIHR) [Grant CIHR-950-231935]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1225 .

ACUTE EFFECTS OF CLUSTER SET AND TRADITIONAL SET POST-ACTIVATION POTENTIATION PROTOCOLS ON VERTICAL JUMP PERFORMANCE

The aim of this study was to investigate the acute effects of different post-activation potential (PAP) protocols on Countermovement Jump (CMJ) performance in female athletes. Twelve elite female taekwondo athletes (age 15.17±.718 years, height 168.66±4.81 cm and body weight 49.25±2.37 kg) participated in the study voluntarily. The resistance training methods of the traditional set (1RM 75% x 3 sets x 12 repetitions and 180 s rest between sets) and cluster set (1RM 75% x 3 sets x 4+4+4 (total 12) repetitions, 30 s rest between sets and 180 s rest between sets) were applied to the research group. CMJ test performances were recorded for 30 s, 4 min and 8 min before and after both PAP protocols. Paired sample t-test was applied for pre-posttest comparison. In the traditional set structure, there was a statistically significant difference between CMJ pre test and CMJ 4 min test. In the cluster set structure, a statistically significant difference was found between CMJ pre test and CMJ30 sec, between CMJ pre test and CMJ 4 min and between CMJ pre test and CMJ 8 min. As a result, the PAP effect is better with the cluster set structure than with the traditional set due to the intermittent rest that will be given with the cluster set structure due to the sudden replenishment of energy stores.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0017/a.php" alt="Hit counter" /></p>

Multiobjective Optimization Design of Interpretable Evolutionary Fuzzy Systems With Type Self-Organizing Learning of Fuzzy Sets

This paper proposes a new multiobjective optimization approach for designing a self-generated interpretable fuzzy logic system (FLS). The types of fuzzy sets can be constructed automatically by self-organizing method, so as to form a hybrid fuzzy system. Different from the existing evolutionary type-1 fuzzy system, which is full of type-1 fuzzy sets, and the evolutionary interval type-2 fuzzy system, which is full of interval type-2 fuzzy sets, there are both type-1 fuzzy sets and interval type-2 fuzzy sets in the hybrid fuzzy system. A new transparency-oriented objective function is defined, and the constraint of the footprint of uncertainty (FOU) of the interval type-2 (IT2) fuzzy set (FS) is considered for the first time. A new FS merging criterion focusing on the proximity of the cores of fuzzy sets is proposed, which is easy to calculate and maintains the characteristics of classical similarity measures. Combined with the new merging criterion, the online cluster and fuzzy set updating (OCFU) algorithm is employed to initialize the reference rule base and the type of fuzzy sets, as it is assumed that no training data are collected in advance. Based on the reference rule base, the advanced multiobjective front-guided continuous ant colony optimization (AMO-FCACO) algorithm is introduced to optimize all the free parameters of the FLS. With the operation mentioned above, the self-generated FLSs achieve a good balance between interpretability and performance. The effectiveness of the proposed method is verified by three nonlinear system tracking problems.

Validity Index for Clustered Data in Non-negative Space

We propose a novel nonparametric cluster validity index which can be used to evaluate the unknown number of existing clusters prevailing a data set, to assess the quality of classification for a clustered set of data members, or to compare the clustering output obtained from different algorithms. Our efficient measure depends only on the observation-wise distances of the non-negative clustered data from their origin given in an arbitrary dimensional space. Its fast implementation makes it appealing for big data analysis, whereas the high-dimensional applicability widens its usefulness. Easy interpretation, simple algorithm, speedy computation and great performance, shown in terms of data study, establish our advised validity index as a strong cluster accuracy measure among the acknowledged ones from the literature. AMS subject classification: 62H30

Fine Tuning the Intermolecular Interactions of Water Clusters Using the Dispersion-Corrected Density Functional Theory.

Dispersion-inclusive density functional theory (DFT) methods have unequivocally demonstrated improved performances with respect to standard DFT approximations for modeling large and extended molecular systems at the quantum mechanical level. Yet, in some cases, disagreements with highly accurate reference calculations, such as CCSD(T) and quantum Monte Carlo (MC) calculations, still remain. Furthermore, the application of general-purpose corrections, such as the popular Grimme's semi-classical models (DFT-D), to different Kohn-Sham exchange-correlation functionals sometimes leads to variable and inconsistent results, which recommend a careful prior evaluation. In a recent study, we proposed a simple optimization protocol for enhancing the accuracy of these DFT-D methods by following an alternative and system-specific approach. Here, adopting the same computational strategy, we show how the accurate MC intermolecular interactions of a large set of water clusters of variable sizes (i.e., 300 (H2O)n structures, n = 9, 15, 27) can be reproduced remarkably well by dispersion-corrected DFT models (i.e., B3LYP-D4, PBE-D4, revPBE(0)-D4) upon re-optimization, reaching a mean absolute error per monomer of ~0.1 kcal/mol. Hence, the obtained results support the use of this procedure for fine-tuning tailored DFT-D models for the accurate description of targeted molecular systems.

Genome Sequence of the Streptomyces Strain AgN23 Revealed Expansion and Acquisition of Gene Repertoires Potentially Involved in Biocontrol Activity and Rhizosphere Colonization

Streptomycetes are gram-positive actinobacteria largely represented in the plant root microbiota. The genetic determinants involved in the adaption of Streptomyces in the rhizosphere environment are mostly unknown but can rely on the ability to release phytohormones, degrade plant cell-wall polysaccharides, and produce specialized metabolites notably involved in microbial competition. Here, we sequenced the genome of the rhizospheric and plant defense-stimulating strain Streptomyces sp. AgN23. We found that it belongs to the soil- and plant root-dwelling S. violaceusniger clade. The genome annotation of AgN23 revealed the genetic potential of the bacterium to degrade the plant cell wall with a large repertoire of carbohydrate degrading enzymes, to synthesize auxin, a major regulator of plant development, and to produce antimicrobials (rustmicin, mediomycin, niphimycin, nigericin) and plant bioactive compounds (nigericin, echosides, elaiophylin) through a set of biosynthetic gene clusters. We also found that these genomic features are well conserved among members of the S. violaceusniger clade. In addition, AgN23 displays original events of biosynthetic gene cluster acquisitions and losses, which may account for its interaction with plants. Taken together, our work supports the hypothesis that evolution of a large set of conserved hydrolytic enzymes directed against plant polymers and specialized metabolite repertoires can mediate the adaptation of S. violaceusniger strains to the rhizospheric ecological niches. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Data-driven deep phenotyping of multiple sclerosis patients using patient-reported outcome measures (P3-3.015)

<h3>Objective:</h3> To identify distinct clusters of persons with multiple sclerosis (PwMS) with shared impairment patterns across 11 functional domains. <h3>Background:</h3> PwMS experience wide-ranging symptoms across multiple domains, alone or in combination, with varied severity. We sought to characterize naturally occurring clusters of MS using seemingly heterogeneous longitudinal patient-reported outcome measure (PROMs) responses, which are increasingly used in clinical trials and practice. <h3>Design/Methods:</h3> From the electronic health records of 6,619 MS patients with extended care (≥2 encounters 6 months apart) at a tertiary referral center between 2008–2015, we abstracted responses for 11 validated MS-PROMs include 8 MS Performance Scales© (mobility, hand function, vision, fatigue, cognition, bladder/bowel, sensory, spasticity) and 3 MS Functional Scales (pain, depression, tremor/coordination). We applied unsupervised machine learning through mixture modeling (latent profile analysis) to the 11 MS-PROMs at baseline, to identify a set of discrete and non-overlapping clusters of MS patients with similar impairment patterns. <h3>Results:</h3> Nine patient clusters were detected, and were differentiated by low (4 clusters), medium (1 cluster), and high (4 clusters) levels of mobility impairment. The low mobility impairment clusters were differentiated by low impairment across domains (31.7%), to moderate fatigue (18.7%), to moderate fatigue + moderately-high sensory dysfunction (7.9%), and to moderately-high fatigue + pain (10%). The medium mobility cluster had moderately-high impairment across domains (10.2%). The clusters with high mobility impairments varied with mild impairment (8.3%) to high impairment (4.1%) across all domains, and two clusters with moderate impairment across domains but not sensory dysfunction (low [3.9%] vs high [5.1%]). The clusters also varied by sociodemographic and clinical attributes. The most impaired cluster had the highest proportion of Black patients, the lowest quality of life, and lived in ZIP codes with the highest levels of deprivation. <h3>Conclusions:</h3> We leveraged PROMs to innovatively deep phenotype MS patients into distinct subgroups with varying levels of disability and symptomatology. <b>Disclosure:</b> The institution of Prof. Briggs has received research support from NIH. The institution of Prof. Briggs has received research support from Michael J. Fox Foundation. Doug Gunzler has nothing to disclose. Dr. Ontaneda has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Novartis. Dr. Ontaneda has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Genentech/Roche. Dr. Ontaneda has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Biogen Idec. Dr. Ontaneda has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Genentech/Roche. The institution of Dr. Ontaneda has received research support from NIH. The institution of Dr. Ontaneda has received research support from PCORI. The institution of Dr. Ontaneda has received research support from NMSS. The institution of Dr. Ontaneda has received research support from Genetech. Dr. Miller has received intellectual property interests from a discovery or technology relating to health care. Mr. Zamora has nothing to disclose. Alessandro De Nadai has nothing to disclose.

EV-Station-Grid Coordination Optimization Strategy Considering Psychological Preferences

This paper proposes the electric vehicle (EV)-station-grid coordination optimization strategy considering user preferences, which regulates the charging behaviors of EV users from the user side to ensure the stable and safe operation of the power grid. Firstly, the spatio-temporal prediction model of charging load based on speed-temperature is developed. The model of EV power consumption per unit mileage affected by temperature and EV speed is constructed, and the shortest path algorithm is applied to determine the driving paths of EVs so as to judge the charging demand in combination with the state of charge (SOC) of the battery and to determine the charging periods and locations of the EVs, thus obtaining the spatio-temporal information of the charging load. Secondly, a multi-attribute charging decision model considering user preferences is constructed. Fuzzy clustering and rough set theory are applied to mine user behavior preferences, combined with behavioral economics to describe users’ irrational charging decision-making psychology. Lastly, a real-time charging price model considering voltage fluctuation index and user charging cost is constructed to analyze the impact of price on guiding charging behaviors. The simulation results verify the effectiveness and performance of the collaborative optimization strategy.

Identification immune response genes in psoriasis after treatment with secukinumab

BackgroundSecukinumab is a fully human IgG1κ MoAb that selectively binds to IL-17A with high affinity, and it has been proven effective for the treatment of psoriasis. However, the immune response pathways and mechanisms during the treatment are still masked. Therefore, the current study was designed to investigate the potential immune response genes via bioinformatics approaches.MethodsGene expression data of severe plaque-type psoriasis was retrieved from the GEO database. Quantification of immune infiltration by ssGSEA and identification of differentially infiltrated immune cells were conducted to validate the treatment effect of secukinumab. After data processing, differentially expressed genes were identified between the treatment and untreated group. TC-seq was employed to analyze the trend of gene expression and clustering analysis. IL-17 therapeutic immune response genes were selected by taking the intersection of the genes inside the key cluster set and the MAD3-PSO geneset. Based on these therapeutic response genes, protein–protein interaction networks were built for key hub gene selection. These hub genes would work as potential immune response genes, and be validated via an external dataset.ResultsEnrichment scores calculated by ssGSEA illustrated that the immune infiltration level of T cells had a strong difference before and after medication, which validated the treatment effect of Secukinumab. 1525 genes that have significantly different expression patterns before and after treatment were extracted for further analysis, and the enrichment result shows that these genes have the function related to epidermal development, differentiation, and keratinocytes differentiation. After overlapping candidate genes with MAD3-PSO gene set, 695 genes were defined as anti-IL7A treatment immune response genes, which were mainly enriched in receptor signaling and IL-17 signaling pathways. Hub gene were pinpointed from the PPI network constructed by anti-IL7A treatment immune response genes, their expression pattern fits TC-seq gene expression pattern.ConclusionOur study revealed the potential anti-IL7A treatment immune response genes, and the central hub genes, which may act critical roles in Secukinumab, induced immune response. This would open up a novel and effective avenue for the treatment of psoriasis.

Single Linkage Weighted Steepest Gradient Adaboost Cluster-Based D2D in 5G Networks

Efficiency of data transmissions with minimum latency levels and better resource utilization is a challenging issue in 5 G device-to-device (D 2D) environments. A novel technique referred to as single linkage steepest gradient gentle AdaBoost cluster-based device (SLSGAC) is introduced to improve device-to-device communications with minimum latency. The proposed technique uses the ensemble clustering approach to group mobile devices by constructing a set of weak clusters, based on the Minkowski single linkage clustering technique. In the weak clustering process, residual energy, bandwidth and SINR are estimated, and mobile devices are grouped based on the Minkowski distance measure. Results of the weak clustering process are combined to provide the final ensemble’s clustering output by applying the steepest gradient function to minimize the error rate. For each cluster, a head is selected from among the group members to improve the data transmission rate and minimize latency. Simulations are conducted comparing the proposed technique with the existing methods based on such metrics as energy efficiency, data delivery ratio, packet loss rate, throughput and latency.

3D interaction homology: The hydrophobic residues alanine, isoleucine, leucine, proline and valine play different structural roles in soluble and membrane proteins.

The aliphatic hydrophobic amino acid residues-alanine, isoleucine, leucine, proline and valine-are among the most common found in proteins. Their structural role in proteins is seemingly obvious: engage in hydrophobic interactions to stabilize secondary, and to a lesser extent, tertiary and quaternary structure. However, favorable hydrophobic interactions involving the sidechains of these residue types are generally less significant than the unfavorable set arising from interactions with polar atoms. Importantly, the constellation of interactions between residue sidechains and their environments can be recorded as three-dimensional maps that, in turn, can be clustered. The clustered average map sets compose a library of interaction profiles encoding interaction strengths, interaction types and the optimal 3D position for the interacting partners. This library is backbone angle-dependent and suggests solvent and lipid accessibility for each unique interaction profile. In this work, in addition to analysis of soluble proteins, a large set of membrane proteins that contained optimized artificial lipids were evaluated by parsing the structures into three distinct components: soluble extramembrane domain, lipid facing transmembrane domain, core transmembrane domain. The aliphatic residues were extracted from each of these sets and passed through our calculation protocol. Notable observations include: the roles of aliphatic residues in soluble proteins and in the membrane protein's soluble domains are nearly identical, although the latter are slightly more solvent accessible; by comparing maps calculated with sidechain-lipid interactions to maps ignoring those interactions, the potential extent of residue-lipid and residue-interactions can be assessed and likely exploited in structure prediction and modeling; amongst these residue types, the levels of lipid engagement show isoleucine as the most engaged, while the other residues are largely interacting with neighboring helical residues.

Optimal vs rotation heuristics in the role of cluster-head for routing in IoT constrained devices

Wireless Sensor Networks (WSNs) are crucial for implementing the Internet of Things (IoT) vision. A WSN can include distributed devices that can be battery powered, have a small amount of memory, have limited CPU processing capabilities and limited transmission range. WSNs are categorised into two main types that are homogeneous and heterogeneous. Homogeneous WSNs are composed of nodes that are all equal while heterogeneous ones are composed of nodes that can have different transmission rate and different initial energy. Collecting data in an energy efficient manner is one of the main goals when building wireless sensor networks since devices are battery powered and battery replacement can be difficult or impossible to be performed. Clustering protocols are one of the main approaches that have been used in order to collect data in an energy efficient way. A clustering protocol groups nodes into a set of clusters. Each cluster has a representative node that is referred to as Cluster Head (CH). This collects data from its cluster members and forwards them to an external Base Station, possibly in a multi-hop way among cluster heads. A rotation strategy for electing the cluster head is often used together with clustering in order to prolong the network lifetime. Rotation eliminates the overhead traffic that is needed for leader election and cluster formation since the old cluster heads directly designate new ones. In this work, we describe an Optimum Rotation Scheduling (ORS) that uses Integer Linear Programming in order to find the optimum rotation strategy. ORS assumes the WSN clusters are already formed by using some clustering protocol. A novel integer linear programming formulation is then used in order to define a cluster head rotation that produces the optimum cluster lifetime. Comparisons with existing heuristics and the provided optimum are then shown and discussed by means of extended simulations involving both homogeneous and heterogeneous WSNs.

The effects of GPR40 agonists on hair growth are mediated by ANGPTL4

GPR40 is found primarily in pancreatic β cells, and is well known to regulate insulin secretion. Despite numerous studies on GPR40, the role and functions of GPR40 related to hair growth are not yet known. The current study investigated hair growth promoting effect of the GPR40 agonists and its mechanism of action using various bio-informatics tools, in vitro and animal experiments. GPR40 may affect the hair cycle, according to clustering and Gene Set Enrichment Analysis (GSEA). Hair growth effect of GPR40 was validated by telogen-to-anagen transition and vibrissae organ culture in the mouse. GPR40 was predominantly expressed in the outer root sheath (ORS) in anagen stage, suggesting that ORS cell is the target of GPR40 agonists. To investigate the mechanism of action for GPR40 agonists’ hair growth effect, Gene Ontology (GO) enrichment analysis was performed and it revealed that GPR40 agonists were associated with angiogenesis. ANGPTL4, known for promoting angiogenesis, was highly up-regulated after GPR40 agonists treatment in the hORS cells, and also increased the proliferation and migration. Furthermore, GPR40 agonists promoted hair growth by inducing angiogenesis via ANGPTL4 in the animal experiment. GPR40 agonists activated MAPK and peroxisome proliferator-activated receptors (PPARγ) pathway in hORS cells, while the inhibition of MAPK pathway attenuated ANGPTL4 expression. Finally, GPR40 agonists increased hair growth via autocrine effects in the ORS cells, and induced angiogenesis through paracrine effects by upregulating ANGPTL4 via p38 and PPARγ pathways. As a result, GPR40 agonists have potential as a therapeutic drug for hair loss treatment.

Differences and Similarities in Climate Change Adaptation Policy Instrument Mixes in Selected European Countries

The increasingly severe effects of climate change have resulted in a shift in countries’ approach to climate policy. From an initial focus on mitigation efforts, adaptation to climate change is now given equal importance. Adaptation policies in individual countries provide for different sets of instruments owing to different natural conditions and climate change impacts and their resulting problems as well as different approaches related to the sociopolitical characteristics of the country. In the paper, we identify and classify adaptation policy instruments and then look for the differences and similarities in the adaptation instrument mixes included in the national adaptation strategic documents of selected European countries. We focused on Western European (WE) and Central and Eastern European (CEE) countries, as the latter are underrepresented in studies on adaptation policies. Based on text-mining methods, i.e., categorisation of policy instruments using a set of criteria and clustering, we looked for similarities and differences between the adaptation instrument mixes in the chosen European countries. We found similarities between the two CEE countries studied—Poland and Lithuania. These countries are also different from WE countries in this regard. The results indicate that CEE countries have a sectoral rather than systemic approach to adaptation policy, and instruments from the management sphere are less prominent.

K-sets and k-swaps algorithms for clustering sets

We present two new clustering algorithms called k-sets and k-swaps for data where each object is a set. First, we define the mean of the sets in a cluster, and the distance between a set and the mean. We then derive the k-sets algorithm from the principles of classical k-means so that it repeats the assignment and update steps until convergence. To the best of our knowledge, the proposed algorithm is the first k-means based algorithm for this kind of data. We adopt the idea also into random swap algorithm, which is a wrapper around the k-means that avoids local minima. This variant is called k-swaps. We show by experiments that this algorithm provides more accurate clustering results than k-medoids and other competitive methods.

Effect of Group-10 Element M (Ni, Pd, Pt) on Electronic Structure of Icosahedral M@Au12 Cores of MAu24L18 (L = Alkynyl, Thiolate)

Heterometal doping into chemically modified gold clusters is a straightforward method to modulate their electronic structure. Although examples exist of alloy clusters containing a series of heterometeal dopants from different groups of the periodic table, it is rare for a complete set of alloy clusters to contain dopants from the same group. In this work, we newly synthesize Ni-doped gold clusters with NiAu24L18 [L = ArFC≡C– (ArF = 3,5-(CF3)2C6H3), PhC2H4S–] by using hydrogen-containing phosphine-protected NiAu clusters as precursors. The synthesis of the Ni-doped counterpart allows us to compare the geometric and electronic structures of prototypical alloy clusters with a M@Au12 core (M = Ni, Pd, Pt). Single-crystal X-ray diffraction analysis indicates that due to the small atomic radius of Ni, NiAu24L18 has a slightly contracted core than clusters with M = Pd or Pt. Voltammetry and optical absorption spectroscopy suggest that the modified jellium model cannot solely explain the period-dependent doping effect because the Ni@Au12 core contracts. MAu24L18 clusters (L = PhC2H4S–) are paramagnetized by single electron reduction, and their magnetic properties are investigated by using electron spin resonance spectroscopy. This work thus provides a guide for designing gold clusters by heterometal doping within a given group of the periodic table.

PARMA-CC: A family of parallel multiphase approximate cluster combining algorithms

Clustering is a common task in data analysis applications. Despite the extensive literature, the continuously increasing volumes of data produced by sensors (e.g., rates of several MB/s by 3D scanners such as LIDAR sensors), and the time-sensitivity of the applications leveraging the clustering outcomes (e.g., detecting critical situations such as detecting boundary crossing from a robot arm that could injure human beings) demand for efficient data clustering algorithms that can effectively utilize the increasing computational capacities of modern hardware. To that end, we leverage approximation and parallelization, where the former is to scale down the amount of data, and the latter is to scale up the computation. Regarding parallelization, we explore a design space for synchronization and workload distribution among the threads. As we study different parts of the design space, we propose representative Parallel Multiphase Approximate Cluster Combining, abbreviated as PARMA-CC, algorithms.We show that PARMA-CC algorithms yield equivalent clustering outcomes despite their different approaches. Furthermore, we show that certain PARMA-CC algorithms can achieve higher efficiency with respect to certain properties of the data to be clustered. Generally speaking, in PARMA-CC algorithms, parallel threads compute summaries associated with clusters of data (sub)sets. As the threads concurrently combine the summaries, they construct a comprehensive summary of the sets of clusters. By approximating a cluster with its respective geometrical summaries, PARMA-CC algorithms scale well with increasing data volumes, and, by computing and efficiently combining the summaries in parallel, they enable latency improvements. PARMA-CC algorithms utilize special data structures that enable parallelism through in-place data processing. As we show in our analysis and evaluation, PARMA-CC algorithms can complement and outperform well-established methods, with significantly better timeliness especially when utilizing multiple threads, while still providing highly accurate results in a variety of data sets, even with skewed data distributions, which cause the traditional approaches to exhibit their worst-case behaviour.

Dynamic network biomarker to determine the critical point of breast cancer stage progression.

The discovery of early warning signs and biomarkers in patients with early breast cancer is crucial for the prevention and treatment of breast cancer. Dynamic Network Biomarker (DNB) is an approach based on nonlinear dynamics theory, which we exploited to identify a set of DNB members and their key genes as early warning signals during breast cancer staging progression. First, based on the gene expression profile of breast cancer in the TCGA database, the DNB algorithm was used to calculate the composite index (CI) of each gene cluster in the process of breast cancer anatomical staging. Then we calculated gene modules associated with the clinical phenotype stage based on weighted gene co-expression network analysis (WGCNA), combined with DNB membership to identify key genes in the network. We identified a set of gene clusters with the highest CI in Stage II as DNBs, whose roles in related pathways indicate the emergence of a tipping point and impact on breast cancer development. In addition, analysis of the key gene GPRIN1 showed that high expression of GPRIN1 predicts poor prognosis, and related immune analysis showed that GPRIN1 is involved in the development of breast cancer through immune aspects. The discovery of DNBs and the key gene GPRIN1 can provide potential biomarkers and therapeutic targets for breast cancer.

From Molecular Oxo-Hydroxo Ce Clusters to Crystalline CeO2

Many studies report the synthesis and characterizations of CeO2 nanoparticles (NPs) due to their applications in catalysis, energy storage, or biomedical fields. In this study, we report a comprehensive interpretation of X-ray measurements on a series of oxo-hydroxo polynuclear Ce complexes whose atomic structures are resolved. The set of investigated samples represent the formation of CeO2 small nanoparticles from a few Ce ions. Such clusters might serve as good building blocks in nanoscale architectures, and many clusters together may perform as highly active small particles. Therefore, the set of Ce clusters with growing size (from 0.6 to 1.2 nm) and nuclearity (from 6 to 38 Ce atoms) were synthesized and characterized by single-crystal X-ray diffraction (XRD), high-energy X-ray scattering (HEXS), and high-energy-resolution fluorescence detection (HERFD) X-ray absorption spectroscopy (XAS) methods and compared to larger CeO2 NPs and bulk CeO2. Methods reveal consistent trends as the size of the system grows from molecular Ce-{n} 2, 6, 24, 38, and 40 clusters to bulk CeO2. HEXS reveals a broadening in distribution for the short Ce–O bonds for the small clusters. Concomitantly, the HERFD-XAS performed at the Ce LIII edge indicates a gradual splitting of the Ce 5d states as the core of the complexes becomes more CeO2-like. The experimental observations have been supported by electronic structure calculations, based on the crystallographic determination of the cluster structures. Theoretical simulations allow us to isolate the structural and electronic properties of individual Ce sites within clusters and mark the great difference between surface and core Ce atoms. It also shows how a combination of simulations from different sites results in the accurate reproduction of the corresponding experimental data. This approach based on the experimentally determined atomic coordinates of clusters was then successfully extended to model Ce LIII edge HERFD-XAS spectra for larger CeO2 NPs. By linking the atomic and electronic structures of Ce polynuclear complexes, CeO2 nanoparticles, and bulk CeO2, this work extends the fundamental knowledge of Ce oxide nanomaterials and supports a better understanding and predictability of their electronic structure.

Influence of Cluster Sets on Mechanical and Perceptual Variables in Adolescent Athletes.

Cluster sets (CS) are effective in maintaining performance and reducing perceived effort compared to traditional sets (TRD). However, little is known about these effects on adolescent athletes. The purpose of this study was to compare the effect of CS on the performance of mechanical and perceptual variables in young athletes. Eleven subjects [4 boys (age = 15.5 ± 0.8 years; body mass = 54.3 ± 7.0 kg; body height = 1.67 ± 0.04 m; Back Squat 1RM/body mass: 1.62 ± 0.19 kg; years from peak height velocity [PHV]: 0.94 ± 0.50) and 7 girls (age = 17.2 ± 1.4 years; body mass = 54.7 ± 6.3 kg; body height = 1.63 ± 0.08 m; Back Squat 1RM/body mass: 1.22 ± 0.16 kg; years from PHV: 3.33 ± 1.00)] participated in a randomized crossover design with one traditional (TRD: 3 × 8, no intra-set and 225 s interest rest) and two clusters (CS1: 3 × 2 × 4, one 30 s intra-set and 180 s inter-set rest; and CS2: 3 × 4 × 2, three 30 s intra-set and 90 s inter-set rest) protocols. The subjects were assessed for a Back Squat 1RM for the first meet, then performed the three protocols on three different days, with at least 48 h between them. During experimental sessions, a back squat exercise was performed, and mean propulsive velocity (MPV), power (MPP), and force (MPF) were collected to analyze performance between protocols, together with measures of countermovement jump (CMJ) and perceptual responses through Rating of Perceived Exertion for each set (RPE-Set) and the overall session (S-RPE), and Muscle Soreness (DOMS). The results showed that velocity and power decline (MVD and MPD) were favorable for CS2 (MVD: -5.61 ± 14.84%; MPD: -5.63 ± 14.91%) against TRD (MVD: -21.10 ± 11.88%; MPD: -20.98 ± 11.85%) (p < 0.01) and CS1 (MVD: -21.44 ± 12.13%; MPD: -21.50 ± 12.20%) (p < 0.05). For RPE-Set, the scores were smaller for CS2 (RPE8: 3.23 ± 0.61; RPE16: 4.32 ± 1.42; RPE24: 4.46 ± 1.51) compared to TRD (RPE8: 4.73 ± 1.33; RPE16: 5.46 ± 1.62; RPE24: 6.23 ± 1.97) (p = 0.008), as well as for Session RPE (CS2: 4.32 ± 1.59; TRD: 5.68 ± 1.75) (p = 0.015). There were no changes for jump height (CMJ: p = 0.985), and the difference between time points in CMJ (ΔCMJ: p = 0.213) and muscle soreness (DOMS: p = 0.437) were identified. Our findings suggest that using CS with a greater number of intra-set rests is more efficient even with the total rest interval equalized, presenting lower decreases in mechanical performance and lower perceptual effort responses.